Relative Entropy in Biological Systems

27 November, 2015

Here’s a draft of a paper for the proceedings of a workshop on Information and Entropy in Biological System this spring:

• John Baez and Blake Pollard, Relative Entropy in Biological Systems.

We’d love any comments or questions you might have. I’m not happy with the title. In the paper we advocate using the term ‘relative information’ instead of ‘relative entropy’—yet the latter is much more widely used, so I feel we need it in the title to let people know what the paper is about!

Here’s the basic idea.

Life relies on nonequilibrium thermodynamics, since in thermal equilibrium there are no flows of free energy. Biological systems are also open systems, in the sense that both matter and energy flow in and out of them. Nonetheless, it is important in biology that systems can sometimes be treated as approximately closed, and sometimes approach equilibrium before being disrupted in one way or another. This can occur on a wide range of scales, from large ecosystems to within a single cell or organelle. Examples include:

• A population approaching an evolutionarily stable state.

• Random processes such as mutation, genetic drift, the diffusion of organisms in an environment or the diffusion of molecules in a liquid.

• A chemical reaction approaching equilibrium.

An interesting common feature of these processes is that as they occur, quantities mathematically akin to entropy tend to increase. Closely related quantities such as free energy tend to decrease. In this review, we explain some mathematical results that make this idea precise.

Most of these results involve a quantity that is variously known as ‘relative information’, ‘relative entropy’, ‘information gain’ or the ‘Kullback–Leibler divergence’. We’ll use the first term. Given two probability distributions p and q on a finite set X, their relative information, or more precisely the information of p relative to q, is

\displaystyle{ I(p\|q) = \sum_{i \in X} p_i \ln\left(\frac{p_i}{q_i}\right) }

We use the word ‘information’ instead of ‘entropy’ because one expects entropy to increase with time, and the theorems we present will say that I(p\|q) decreases with time under various conditions. The reason is that the Shannon entropy

\displaystyle{ S(p) = -\sum_{i \in X} p_i \ln p_i }

contains a minus sign that is missing from the definition of relative information.

Intuitively, I(p\|q) is the amount of information gained when we start with a hypothesis given by some probability distribution q and then learn the ‘true’ probability distribution p. For example, if we start with the hypothesis that a coin is fair and then are told that it landed heads up, the relative information is \ln 2, so we have gained 1 bit of information. If however we started with the hypothesis that the coin always lands heads up, we would have gained no information.

We put the word ‘true’ in quotes here, because the notion of a ‘true’ probability distribution, which subjective Bayesians reject, is not required to use relative information. A more cautious description of relative information is that it is a divergence: a way of measuring the difference between probability distributions that obeys

I(p \| q) \ge 0


I(p \| q) = 0 \iff p = q

but not necessarily the other axioms for a distance function, symmetry and the triangle inequality, which indeed fail for relative information.

There are many other divergences besides relative information, some of which we discuss in Section 6. However, relative information can be singled out by a number of characterizations, including one based on ideas from Bayesian inference. The relative information is also close to the expected number of extra bits required to code messages distributed according to the probability measure p using a code optimized for messages distributed according to q.

In this review, we describe various ways in which a population or probability distribution evolves continuously according to some differential equation. For all these differential equations, I describe conditions under which relative information decreases. Briefly, the results are as follows. We hasten to reassure the reader that our paper explains all the jargon involved, and the proofs of the claims are given in full:

• In Section 2, we consider a very general form of the Lotka–Volterra equations, which are a commonly used model of population dynamics. Starting from the population P_i of each type of replicating entity, we can define a probability distribution

p_i = \frac{P_i}{\displaystyle{\sum_{i \in X} P_i }}

which evolves according to a nonlinear equation called the replicator equation. We describe a necessary and sufficient condition under which I(q\|p(t)) is nonincreasing when p(t) evolves according to the replicator equation while q is held fixed.

• In Section 3, we consider a special case of the replicator equation that is widely studied in evolutionary game theory. In this case we can think of probability distributions as mixed strategies in a two-player game. When q is a dominant strategy, $I(q|p(t))$ can never increase when p(t) evolves according to the replicator equation. We can think of I(q\|p(t)) as the information that the population has left to learn.
Thus, evolution is analogous to a learning process—an analogy that in the field of artificial intelligence is exploited by evolutionary algorithms.

• In Section 4 we consider continuous-time, finite-state Markov processes. Here we have probability distributions on a finite set X evolving according to a linear equation called the master equation. In this case I(p(t)\|q(t)) can never increase. Thus, if q is a steady state solution of the master equation, both I(p(t)\|q) and I(q\|p(t)) are nonincreasing. We can always write q as the Boltzmann distribution for some energy function E : X \to \mathbb{R}, meaning that

\displaystyle{ q_i = \frac{\exp(-E_i / k T)}{\displaystyle{\sum_{j \in X} \exp(-E_j / k T)}} }

where T is temperature and k is Boltzmann’s constant. In this case, I(p(t)\|q) is proportional to a difference of free energies:

\displaystyle{ I(p(t)\|q) = \frac{F(p) - F(q)}{T} }

Thus, the nonincreasing nature of I(p(t)\|q) is a version of the Second Law of Thermodynamics.

• In Section 5, we consider chemical reactions and other processes described by reaction networks. In this context we have populations P_i of entities of various kinds i \in X, and these populations evolve according to a nonlinear equation called the rate equation. We can generalize relative information from probability distributions to populations by setting

\displaystyle{ I(P\|Q) = \sum_{i \in X} P_i \ln\left(\frac{P_i}{Q_i}\right) - \left(P_i - Q_i\right) }

If Q is a special sort of steady state solution of the rate equation, called a complex balanced equilibrium, I(P(t)\|Q) can never increase when P(t) evolves according to the rate equation.

• Finally, in Section 6, we consider a class of functions called f-divergences which include relative information as a special case. For any convex function f : [0,\infty) \to [0,\infty), the f-divergence of two probability distributions p, q : X \to [0,1] is given by

\displaystyle{ I_f(p\|q) = \sum_{i \in X} q_i f\left(\frac{p_i}{q_i}\right)}

Whenever p(t) and q(t) are probability distributions evolving according to the master equation of some Markov process, I_f(p(t)\|q(t)) is nonincreasing. The f-divergence is also well-defined for populations, and nonincreasing for two populations that both evolve according to the master equation.

A Compositional Framework for Markov Processes

4 September, 2015


This summer my students Brendan Fong and Blake Pollard visited me at the Centre for Quantum Technologies, and we figured out how to understand open continuous-time Markov chains! I think this is a nice step towards understanding the math of living systems.

Admittedly, it’s just a small first step. But I’m excited by this step, since Blake and I have been trying to get this stuff to work for a couple years, and it finally fell into place. And we think we know what to do next.

Here’s our paper:

• John C. Baez, Brendan Fong and Blake S. Pollard, A compositional framework for open Markov processes.

And here’s the basic idea…

Open detailed balanced Markov processes

A continuous-time Markov chain is a way to specify the dynamics of a population which is spread across some finite set of states. Population can flow between the states. The larger the population of a state, the more rapidly population flows out of the state. Because of this property, under certain conditions the populations of the states tend toward an equilibrium where at any state the inflow of population is balanced by its outflow.

In applications to statistical mechanics, we are often interested in equilibria such that for any two states connected by an edge, say i and j, the flow from i to j equals the flow from j to i. A continuous-time Markov chain with a chosen equilibrium having this property is called ‘detailed balanced‘.

I’m getting tired of saying ‘continuous-time Markov chain’, so from now on I’ll just say ‘Markov process’, just because it’s shorter. Okay? That will let me say the next sentence without running out of breath:

Our paper is about open detailed balanced Markov processes.

Here’s an example:

The detailed balanced Markov process itself consists of a finite set of states together with a finite set of edges between them, with each state i labelled by an equilibrium population q_i >0, and each edge e labelled by a rate constant r_e > 0.

These populations and rate constants are required to obey an equation called the ‘detailed balance condition’. This equation means that in equilibrium, the flow from i to j equal the flow from j to i. Do you see how it works in this example?

To get an ‘open’ detailed balanced Markov process, some states are designated as inputs or outputs. In general each state may be specified as both an input and an output, or as inputs and outputs multiple times. See how that’s happening in this example? It may seem weird, but it makes things work better.

People usually say Markov processes are all about how probabilities flow from one state to another. But we work with un-normalized probabilities, which we call ‘populations’, rather than probabilities that must sum to 1. The reason is that in an open Markov process, probability is not conserved: it can flow in or out at the inputs and outputs. We allow it to flow both in and out at both the input states and the output states.

Our most fundamental result is that there’s a category \mathrm{DetBalMark} where a morphism is an open detailed balanced Markov process. We think of it as a morphism from its inputs to its outputs.

We compose morphisms in \mathrm{DetBalMark} by identifying the output states of one open detailed balanced Markov process with the input states of another. The populations of identified states must match. For example, we may compose this morphism N:

with the previously shown morphism M to get this morphism M \circ N:

And here’s our second most fundamental result: the category \mathrm{DetBalMark} is actually a dagger compact category. This lets us do other stuff with open Markov processes. An important one is ‘tensoring’, which lets us take two open Markov processes like M and N above and set them side by side, giving M \otimes N:

The so-called compactness is also important. This means we can take some inputs of an open Markov process and turn them into outputs, or vice versa. For example, using the compactness of \mathrm{DetBalMark} we can get this open Markov process from M:

In fact all the categories in our paper are dagger compact categories, and all our functors preserve this structure. Dagger compact categories are a well-known framework for describing systems with inputs and outputs, so this is good.

The analogy to electrical circuits

In a detailed balanced Markov process, population can flow along edges. In the detailed balanced equilibrium, without any flow of population from outside, the flow along from state i to state j will be matched by the flow back from j to i. The populations need to take specific values for this to occur.

In an electrical circuit made of linear resistors, charge can flow along wires. In equilibrium, without any driving voltage from outside, the current along each wire will be zero. The potentials will be equal at every node.

This sets up an analogy between detailed balanced continuous-time Markov chains and electrical circuits made of linear resistors! I love analogy charts, so this makes me very happy:

    Circuits    Detailed balanced Markov processes
potential population
current flow
conductance rate constant
power dissipation

This analogy is already well known. Schnakenberg used it in his book Thermodynamic Network Analysis of Biological Systems. So, our main goal is to formalize and exploit it. This analogy extends from systems in equilibrium to the more interesting case of nonequilibrium steady states, which are the main topic of our paper.

Earlier, Brendan and I introduced a way to ‘black box’ a circuit and define the relation it determines between potential-current pairs at the input and output terminals. This relation describes the circuit’s external behavior as seen by an observer who can only perform measurements at the terminals.

An important fact is that black boxing is ‘compositional’: if one builds a circuit from smaller pieces, the external behavior of the whole circuit can be determined from the external behaviors of the pieces. For category theorists, this means that black boxing is a functor!

Our new paper with Blake develops a similar ‘black box functor’ for detailed balanced Markov processes, and relates it to the earlier one for circuits.

When you black box a detailed balanced Markov process, you get the relation between population–flow pairs at the terminals. (By the ‘flow at a terminal’, we more precisely mean the net population outflow.) This relation holds not only in equilibrium, but also in any nonequilibrium steady state. Thus, black boxing an open detailed balanced Markov process gives its steady state dynamics as seen by an observer who can only measure populations and flows at the terminals.

The principle of minimum dissipation

At least since the work of Prigogine, it’s been widely accepted that a large class of systems minimize entropy production in a nonequilibrium steady state. But people still fight about the the precise boundary of this class of systems, and even the meaning of this ‘principle of minimum entropy production’.

For detailed balanced open Markov processes, we show that a quantity we call the ‘dissipation’ is minimized in any steady state. This is a quadratic function of the populations and flows, analogous to the power dissipation of a circuit made of resistors. We make no claim that this quadratic function actually deserves to be called ‘entropy production’. Indeed, Schnakenberg has convincingly argued that they are only approximately equal.

But still, the ‘dissipation’ function is very natural and useful—and Prigogine’s so-called ‘entropy production’ is also a quadratic function.

Black boxing

I’ve already mentioned the category \mathrm{DetBalMark}, where a morphism is an open detailed balanced Markov process. But our paper needs two more categories to tell its story! There’s the category of circuits, and the category of linear relations.

A morphism in the category \mathrm{Circ} is an open electrical circuit made of resistors: that is, a graph with each edge labelled by a ‘conductance’ c_e > 0, together with specified input and output nodes:

A morphism in the category \mathrm{LinRel} is a linear relation L : U \leadsto V between finite-dimensional real vector spaces U and V. This is nothing but a linear subspace L \subseteq U \oplus V. Just as relations generalize functions, linear relations generalize linear functions!

In our previous paper, Brendan and I introduced these two categories and a functor between them, the ‘black box functor’:

\blacksquare : \mathrm{Circ} \to \mathrm{LinRel}

The idea is that any circuit determines a linear relation between the potentials and net current flows at the inputs and outputs. This relation describes the behavior of a circuit of resistors as seen from outside.

Our new paper introduces a black box functor for detailed balanced Markov processes:

\square : \mathrm{DetBalMark} \to \mathrm{LinRel}

We draw this functor as a white box merely to distinguish it from the other black box functor. The functor \square maps any detailed balanced Markov process to the linear relation obeyed by populations and flows at the inputs and outputs in a steady state. In short, it describes the steady state behavior of the Markov process ‘as seen from outside’.

How do we manage to black box detailed balanced Markov processes? We do it using the analogy with circuits!

The analogy becomes a functor

Every analogy wants to be a functor. So, we make the analogy between detailed balanced Markov processes and circuits precise by turning it into a functor:

K : \mathrm{DetBalMark} \to \mathrm{Circ}

This functor converts any open detailed balanced Markov process into an open electrical circuit made of resistors. This circuit is carefully chosen to reflect the steady-state behavior of the Markov process. Its underlying graph is the same as that of the Markov process. So, the ‘states’ of the Markov process are the same as the ‘nodes’ of the circuit.

Both the equilibrium populations at states of the Markov process and the rate constants labelling edges of the Markov process are used to compute the conductances of edges of this circuit. In the simple case where the Markov process has exactly one edge from any state i to any state j, the rule is this:

C_{i j} = H_{i j} q_j


q_j is the equilibrium population of the jth state of the Markov process,

H_{i j} is the rate constant for the edge from the jth state to the ith state of the Markov process, and

C_{i j} is the conductance (that is, the reciprocal of the resistance) of the wire from the jth node to the ith node of the resulting circuit.

The detailed balance condition for Markov processes says precisely that the matrix C_{i j} is symmetric! This is just right for an electrical circuit made of resistors, since it means that the resistance of the wire from node i to node j equals the resistance of the same wire in the reverse direction, from node j to node i.

A triangle of functors

If you paid careful attention, you’ll have noticed that I’ve described a triangle of functors:

And if you know anything about how category theorists think, you’ll be wondering if this diagram commutes.

In fact, this triangle of functors does not commute! However, a general lesson of category theory is that we should only expect diagrams of functors to commute up to natural isomorphism, and this is what happens here:

The natural transformation \alpha ‘corrects’ the black box functor for resistors to give the one for detailed balanced Markov processes.

The functors \square and \blacksquare \circ K are actually equal on objects. An object in \mathrm{DetBalMark} is a finite set X with each element i \in X labelled a positive populations q_i. Both functors map this object to the vector space \mathbb{R}^X \oplus \mathbb{R}^X. For the functor \square, we think of this as a space of population-flow pairs. For the functor \blacksquare \circ K, we think of it as a space of potential-current pairs. The natural transformation \alpha then gives a linear relation

\alpha_{X,q} : \mathbb{R}^X \oplus \mathbb{R}^X \leadsto \mathbb{R}^X \oplus \mathbb{R}^X

in fact an isomorphism of vector spaces, which converts potential-current pairs into population-flow pairs in a manner that depends on the q_i. I’ll skip the formula; it’s in the paper.

But here’s the key point. The naturality of \alpha actually allows us to reduce the problem of computing the functor \square to the problem of computing \blacksquare. Suppose

M: (X,q) \to (Y,r)

is any morphism in \mathrm{DetBalMark}. The object (X,q) is some finite set X labelled by populations q, and (Y,r) is some finite set Y labelled by populations r. Then the naturality of \alpha means that this square commutes:

Since \alpha_{X,q} and \alpha_{Y,r} are isomorphisms, we can solve for the functor \square as follows:

\square(M) = \alpha_Y \circ \blacksquare K(M) \circ \alpha_X^{-1}

This equation has a clear intuitive meaning! It says that to compute the behavior of a detailed balanced Markov process, namely \square(f), we convert it into a circuit made of resistors and compute the behavior of that, namely \blacksquare K(f). This is not equal to the behavior of the Markov process, but we can compute that behavior by converting the input populations and flows into potentials and currents, feeding them into our circuit, and then converting the outputs back into populations and flows.

What we really do

So that’s a sketch of what we do, and I hope you ask questions if it’s not clear. But I also hope you read our paper! Here’s what we actually do in there. After an introduction and summary of results:

• Section 3 defines open Markov processes and the open master equation.

• Section 4 introduces detailed balance for open Markov

• Section 5 recalls the principle of minimum power
for open circuits made of linear resistors, and explains how to black box them.

• Section 6 introduces the principle of minimum dissipation for open detailed balanced Markov processes, and describes how to black box these.

• Section 7 states the analogy between circuits and detailed balanced Markov processes in a formal way.

• Section 8 describes how to compose open Markov processes, making them into the morphisms of a category.

• Section 9 does the same for detailed balanced Markov processes.

• Section 10 describes the ‘black box functor’ that sends any open detailed balanced Markov process to the linear relation describing its external behavior, and recalls the similar functor for circuits.

• Section 11 makes the analogy between between open detailed balanced Markov processes and open circuits even more formal, by making it into a functor. We prove that together with the two black box functors, this forms a triangle that commutes up to natural isomorphism.

• Section 12 is about geometric aspects of this theory. We show that the linear relations in the image of these black box functors are Lagrangian relations between symplectic vector spaces. We also show that the master equation can be seen as a gradient flow equation.

• Section 13 is a summary of what we have learned.

Finally, Appendix A is a quick tutorial on decorated cospans. This is a key mathematical tool in our work, developed by Brendan in an earlier paper.

The Game of Googol

20 July, 2015

Here’s a puzzle from a recent issue of Quanta, an online science magazine:

Puzzle 1: I write down two different numbers that are completely unknown to you, and hold one in my left hand and one in my right. You have absolutely no idea how I generated these two numbers. Which is larger?

You can point to one of my hands, and I will show you the number in it. Then you can decide to either select the number you have seen or switch to the number you have not seen, held in the other hand. Is there a strategy that will give you a greater than 50% chance of choosing the larger number, no matter which two numbers I write down?

At first it seems the answer is no. Whatever number you see, the other number could be larger or smaller. There’s no way to tell. So obviously you can’t get a better than 50% chance of picking the hand with the largest number—even if you’ve seen one of those numbers!

But “obviously” is not a proof. Sometimes “obvious” things are wrong!

It turns out that, amazingly, the answer to the puzzle is yes! You can find a strategy to do better than 50%. But the strategy uses randomness. So, this puzzle is a great illustration of the power of randomness.

If you want to solve it yourself, stop now or read Quanta magazine for some clues—they offered a small prize for the best answer:

• Pradeep Mutalik, Can information rise from randomness?, Quanta, 7 July 2015.

Greg Egan gave a nice solution in the comments to this magazine article, and I’ll reprint it below along with two followup puzzles. So don’t look down there unless you want a spoiler.

I should add: the most common mistake among educated readers seems to be assuming that the first player, the one who chooses the two numbers, chooses them according to some probability distribution. Don’t assume that. They are simply arbitrary numbers.

The history of this puzzle

I’d seen this puzzle before—do you know who invented it? On G+, Hans Havermann wrote:

I believe the origin of this puzzle goes back to (at least) John Fox and Gerald Marnie’s 1958 betting game ‘Googol’. Martin Gardner mentioned it in his February 1960 column in Scientific American. Wikipedia mentions it under the heading ‘Secretary problem’. Gardner suggested that a variant of the game was proposed by Arthur Cayley in 1875.

Actually the game of Googol is a generalization of the puzzle that we’ve been discussing. Martin Gardner explained it thus:

Ask someone to take as many slips of paper as he pleases, and on each slip write a different positive number. The numbers may range from small fractions of 1 to a number the size of a googol (1 followed by a hundred 0s) or even larger. These slips are turned face down and shuffled over the top of a table. One at a time you turn the slips face up. The aim is to stop turning when you come to the number that you guess to be the largest of the series. You cannot go back and pick a previously turned slip. If you turn over all the slips, then of course you must pick the last one turned.

So, the puzzle I just showed you is the special case when there are just 2 slips of paper. I seem to recall that Gardner incorrectly dismissed this case as trivial!

There’s been a lot of work on Googol. Julien Berestycki writes:

I heard about this puzzle a few years ago from Sasha Gnedin. He has a very nice paper about this

• Alexander V. Gnedin, A solution to the game of Googol, Annals of Probability (1994), 1588–1595.

One of the many beautiful ideas in this paper is that it asks what is the best strategy for the guy who writes the numbers! It also cites a paper by Gnedin and Berezowskyi (of oligarchic fame). 

Egan’s solution

Okay, here is Greg Egan’s solution, paraphrased a bit:

Pick some function f : \mathbb{R} \to \mathbb{R} such that:

\displaystyle{ \lim_{x \to -\infty} f(x) = 0 }

\displaystyle{ \lim_{x \to +\infty} f(x) = 1 }

f is strictly increasing: if x > y then f(x) > f(y)

There are lots of functions like this, for example

\displaystyle{f(x) = \frac{e^x}{e^x + 1} }

Next, pick one of the first player’s hands at random. If the number you are shown is a, compute f(a). Then generate a uniformly distributed random number z between 0 and 1. If z is less than or equal to f(a) guess that x is the larger number, but if z is greater than f(a) guess that the larger number is in the other hand.

The probability of guessing correctly can be calculated as the probability of seeing the larger number initially and then, correctly, sticking with it, plus the probability of seeing the smaller number initially and then, correctly, choosing the other hand.

Say the larger number is x and the smaller one is y. Then the probability of guessing correctly is

\frac{1}{2} f(x) + \frac{1}{2} (1 - f(y)) =  \frac{1}{2} + \frac{1}{2} (f(x) - f(y))

This is strictly greater than \frac{1}{2} since x > y so f(x) - f(y) > 0.

So, you have a more than 50% chance of winning! But as you play the game, there’s no way to tell how much more than 50%. If the numbers on the other players hands are very large, or very small, your chance will be just slightly more than 50%.

Followup puzzles

Here are two more puzzles:

Puzzle 2: Prove that no deterministic strategy can guarantee you have a more than 50% chance of choosing the larger number.

Puzzle 3: There are perfectly specific but ‘algorithmically random’ sequences of bits, which can’t predicted well by any program. If we use these to generate a uniform algorithmically random number between 0 and 1, and use the strategy Egan describes, will our chance of choosing the larger number be more than 50%, or not?

But watch out—here come Egan’s solutions to those!


Egan writes:

Here are my answers to your two puzzles on G+.

Puzzle 2: Prove that no deterministic strategy can guarantee you have a more than 50% chance of choosing the larger number.

Answer: If we adopt a deterministic strategy, that means there is a function S: \mathbb{R} \to \{0,1\} that tells us whether on not we stick with the number x when we see it. If S(x)=1 we stick with it, if S(x)=0 we swap it for the other number.

If the two numbers are x and y, with x > y, then the probability of success will be:

P = 0.5 + 0.5(S(x)-S(y))

This is exactly the same as the formula we obtained when we stuck with x with probability f(x), but we have specialised to functions S valued in \{0,1\}.

We can only guarantee a more than 50% chance of choosing the larger number if S is monotonically increasing everywhere, i.e. S(x) > S(y) whenever x > y. But this is impossible for a function valued in \{0,1\}. To prove this, define x_0 to be any number in [1,2] such that S(x_0)=0; such an x_0 must exist, otherwise S would be constant on [1,2] and hence not monotonically increasing. Similarly define x_1 to be any number in [-2,-1] such that S(x_1) = 1. We then have x_0 > x_1 but S(x_0) < S(x_1).

Puzzle 3: There are perfectly specific but ‘algorithmically random’ sequences of bits, which can’t predicted well by any program. If we use these to generate a uniform algorithmically random number between 0 and 1, and use the strategy Egan describes, will our chance of choosing the larger number be more than 50%, or not?

Answer: As Philip Gibbs noted, a deterministic pseudo-random number generator is still deterministic. Using a specific sequence of algorithmically random bits

(b_1, b_2, \dots )

to construct a number z between 0 and 1 means z takes on the specific value:

z_0 = \sum_i b_i 2^{-i}

So rather than sticking with x with probability f(x) for our monotonically increasing function f, we end up always sticking with x if z_0 \le f(x), and always swapping if z_0 > f(x). This is just using a function S:\mathbb{R} \to \{0,1\} as in Puzzle 2, with:

S(x) = 0 if x < f^{-1}(z_0)

S(x) = 1 if x \ge f^{-1}(z_0)

So all the same consequences as in Puzzle 2 apply, and we cannot guarantee a more than 50% chance of choosing the larger number.

Puzzle 3 emphasizes the huge gulf between ‘true randomness’, where we only have a probability distribution of numbers z, and the situation where we have a specific number z_0, generated by any means whatsoever.

We could generate z_0 using a pseudorandom number generator, radioactive decay of atoms, an oracle whose randomness is certified by all the Greek gods, or whatever. No matter how randomly z_0 is generated, once we have it, we know there exist choices for the first player that will guarantee our defeat!

This may seem weird at first, but if you think about simple games of luck you’ll see it’s completely ordinary. We can have a more than 50% chance of winning such a game even if for any particular play we make the other player has a move that ensures our defeat. That’s just how randomness works.

The Large-Number Limit for Reaction Networks (Part 3)

8 October, 2014

joint with Arjun Jain

We used to talk about reaction networks quite a lot here. When Arjun Jain was visiting the CQT, we made a lot of progress understanding how the master equation reduces to the rate equation in the limit where there are very large numbers of things of each kind. But we never told you the end of the story, and by now it’s been such a long time that you may need a reminder of some basic facts!


The rate equation treats the number of things of each kind as continuous—a nonnegative real number—and says how it changes in a deterministic way.

The master equation treats the number of things of each kind as discrete—a nonnegative integer—and says how it changes in a probabilistic way.

You can think of the master equation as the ‘true’ description, and the rate equation as an approximation that’s good in some limit where there are large numbers of molecules — or more precisely, where the probability distribution of having some number of molecules of each kind is sharply peaked near some large value.

You may remember that in the master equation, the state of a chemical system is described by a vector \psi in a kind of ‘Fock space’, while time evolution is described with the help of an operator on this space, called the ‘Hamiltonian’ H:

\displaystyle{ \frac{d}{dt} \psi(t) = H \psi(t) }

The Hamiltonian is built from annihilation and creation operators, so all of this looks very much like quantum field theory. The details are here, and we won’t try to review them all:

• John Baez and Jacob Biamonte, Quantum Techniques for Stochastic Mechanics.

The point is this: the ‘large-number limit’ where the master equation reduces to the rate equation smells a lot like the ‘classical limit’ of quantum field theory, where the description of light in terms of photons reduces to the good old Maxwell equations. So, maybe we can understand the large-number limit by borrowing techniques from quantum field theory!

How do we take the classical limit of quantum electromagnetism and get the classical Maxwell equations? For simplicity let’s ignore charged particles and consider the ‘free electromagnetic field’: just photons, described by the quantum version of Maxwell’s equations. When we take the classical limit we let Planck’s constant \hbar go to zero: that much is obvious. However, that’s not all! The energy of each photon is proportional to \hbar, so to take the classical limit and get a solution of the classical Maxwell’s equations with nonzero energy we also need to increase the number of photons. We cleverly do this in such a way that the total energy remains constant as \hbar \to 0.

So, in quantum electromagnetism the classical limit is also a large-number limit!

That’s a good sign. It suggests the same math will also apply to our reaction network problem.

But then we hit an apparent roadblock. What’s the analogue of Planck’s constant in chemical reaction networks? What should go to zero?

We told you the answer to that puzzle a while ago: it’s the reciprocal of Avogadro’s number!

You see, chemists measure numbers of molecules in ‘moles’. There’s a large number of molecules in each mole: Avogadro’s number. If we let the reciprocal of Avogadro’s number go to zero, we are taking a limit where chemistry becomes ‘continuous’ and the discreteness of molecules goes away. Of course this is just a mathematical trick, but it’s a very useful one.

So, we got around that roadblock. And then something nice happened.

When taking the classical limit of quantum electromagnetism, we focus attention on certain quantum states that are the ‘best approximations’ to classical states. These are called ‘coherent states’, and it’s very easy to study how the behave as we simultaneously let \hbar \to 0 and let the expected number of photons go to infinity.

And the nice thing is that these coherent states are also important in chemistry! But because chemistry involves probabilities rather than amplitudes, they have a different name: ‘Poisson distributions’. On this blog, Brendan Fong used them to give a new proof of a great result in mathematical chemistry, the Anderson–Craciun–Kurtz theorem.

So, we have most of the concepts and tools in place, and we can tackle the large-number limit using quantum techniques.

You can review the details here:

The large-number limit for reaction networks (part 1).

The large-number limit for reaction networks (part 2) .

So, after a quick refresher on the notation, we’ll plunge right in.

As you’ll see, we solve the problem except for one important technical detail: passing a derivative through a limit! This means our main result is not a theorem. Rather, it’s an idea for how to prove a theorem. Or if we act like physicists, we can call it a theorem.

Review of notation

The rate equation says

\displaystyle{\frac{d}{dt}{x}(t) = \sum_{\tau \in T} r(\tau) (t(\tau)-s(\tau)) {x}(t)^{s(\tau)} }


x(t) \in \mathbb{R}^k is a vector describing concentrations of k different species at time t. In chemistry these species could be different kinds of molecules.

• Each \tau \in T is a transition, or in chemistry, a reaction.

s(\tau) \in \mathbb{N}^k is a vector of natural numbers saying how many items of each species appear as inputs to the reaction \tau. This is called the source of the reaction.

t(\tau) \in \mathbb{N}^k is a vector of natural numbers saying how many items of each species appear as outputs of the reaction \tau. This is called the target of the reaction. So, t(\tau) - s(\tau) says the net change of the number of items of each species in the reaction \tau.

• The rate at which the reaction \tau occurs is proportional to the rate constant r(\tau) times the number


Here we are raising a vector to a vector power and getting a number, using this rule:

x^r = x_1^{r_1} \cdots x_k^{r_k}

where r is any vector of natural numbers (r_1, \dots, r_k), and x is any vector of nonnegative real numbers. From now on we’ll call a vector of natural numbers a multi-index.

In this paper:

• John Baez, Quantum techniques for reaction networks.

it was shown that the master equation implies

\displaystyle{\frac{d}{dt}\langle N_i \psi(t)\rangle = \sum_{\tau \in T} r(\tau) (t_i(\tau)-s_i(\tau)) \; \langle N^{\, \underline{s(\tau)}} \, \psi(t)\rangle }


\psi(t) is the stochastic state saying the probability of having any particular number of items of each species at each time t. We won’t review the precise details of how this work; for that reread the relevant bit of Part 8.

N_i is the ith number operator, defined using annihilation and creation operators as in quantum mechanics:

N_i = a_i^\dagger a_i

For the annihilation and creation operators, see Part 8.

\langle N_i \psi(t) \rangle is the expected number of items of the ith species at time t.

• Similarly, \langle N^{\underline{s(\tau)}} \psi(t)\rangle is the expected value of a certain product of operators. For any multi-index r we define the falling power

N_i^{\underline{r}_i} = N_i (N_i - 1) (N_i - 2) \cdots (N_i - r_i +1)

and then we define

N^{\underline{r}} = N_1^{\underline{r_1}} \cdots N_k^{\underline{r_k}}

The large-number limit

Okay. Even if you don’t understand any of what we just said, you’ll see the master and rate equation look similar. The master equation implies this:

\displaystyle{\frac{d}{dt}\langle N_i \psi(t)\rangle = \sum_{\tau \in T} r(\tau) (t_i(\tau)-s_i(\tau)) \; \langle N^{\, \underline{s(\tau)}} \, \psi(t)\rangle }

while the rate equation says this:

\displaystyle{\frac{d}{dt}{x}(t) = \sum_{\tau \in T} r(\tau) (t(\tau)-s(\tau)) \; {x}(t)^{s(\tau)} }

So, we will try to get from the first to the second second with the help of a ‘large-number limit’.

We start with a few definitions. We introduce an adjustable dimensionless quantity which we call \hbar. This is just a positive number, which has nothing to do with quantum theory except that we’re using a mathematical analogy to quantum mechanics to motivate everything we’re doing.

Definition. The rescaled number operators are defined as \widetilde{N}_i = \hbar N_i. This can be thought of as a rescaling of the number of objects, so that instead of counting objects individually, we count them in bunches of size 1/\hbar.

Definition. For any multi-index r we define the rescaled falling power of the number operator N_i by:

\widetilde{N}_i^{\underline{r_i}} = \widetilde{N}_i (\widetilde{N}_i - \hbar)(\widetilde{N}_i-2\hbar)\cdots (\widetilde{N}_i-r_i\hbar+\hbar)

and also define

\widetilde{N}^{\underline{r}} = \widetilde{N}_1^{\underline{r_1}} \; \cdots \;\widetilde{N}_k^{\underline{r_k}}

for any multi-index r.

Using these, we get the following equation:

\displaystyle{\frac{1}{\hbar}\frac{d}{dt} \langle \widetilde{N}_i \psi(t)\rangle = \sum_{\tau \in T} r(\tau) (t_i(\tau)-s_i(\tau)) \; \langle \widetilde{N}^{\underline{s(\tau)}} \psi(t)\rangle \; \frac{1}{\hbar^{|s(\tau)|}} }

where for any multi-index r we set

|r| = r_1 + \cdots + r_k

This suggests a way to rescale the rate constants in the master equation:

Definition. The rescaled rate constants are

\displaystyle{\widetilde{r}(\tau) = \frac{r(\tau)}{\hbar^{|s(\tau)|-1}}}

From here onwards, we change our viewpoint. We consider the rescaled rate constants \widetilde{r}(\tau) to be fixed, instead of the original rate constants r(\tau). So, as we decrease \hbar, we are studying situations where the original rate constants change to ensure that the rescaled rate constants stays fixed!

So, we switch to working with a rescaled master equation:

Definition. The rescaled master equation is:

\displaystyle{\frac{d}{dt} \langle \widetilde{N}_i\widetilde{\psi}(t)\rangle = \sum_{\tau \in T} \widetilde{r}(\tau) (t_i(\tau)-s_i(\tau)) \; \langle \widetilde{N}^{\underline{s(\tau)}} \widetilde{\psi}(t)\rangle }

This is really a one-parameter family of equations, depending on \hbar\in (0,\infty). We write a solution of the rescaled master equation as \widetilde{\psi}(t), but it is really one solution for each value of \hbar.

Following the same procedure as above, we can rescale the rate equation, using the same definition of the rescaled rate constants:

Definition. The rescaled number of objects of the i\mathrm{th} species is defined as \widetilde{x_i}=\hbar x_i, where x_i is the original number of objects of the i\mathrm{th} species. Here again, we are counting in bunches of 1/\hbar.

Using this to rescale the rate equation, we get

Definition. The rescaled rate equation is

\displaystyle{\frac{d}{dt}\widetilde{x}(t) = \sum_{\tau \in T} \widetilde{r}(\tau) (t(\tau)-s(\tau))\; \widetilde{x}(t)^{s(\tau)} }


\widetilde{x}(t)=(\widetilde{x_1}(t),\widetilde{x_2}(t),\dots, \widetilde{x_k}(t))

Therefore, to go from the rescaled master equation to the rescaled rate equation, we require that

\langle\widetilde{N}^{\underline{r}}\, \widetilde{\psi}(t)\rangle \to \langle\widetilde{N}\widetilde{\psi}(t)\rangle^r

as \hbar \to 0. If this holds, we can identify \langle\widetilde{N}\widetilde{\psi}(t)\rangle with \widetilde{x}(t) and get the rate equation from the master equation!

To this end, we introduce the following crucial idea:

Definition. A semiclassical family of states, \widetilde{\psi}, is defined as a one-parameter family of states depending on \hbar \in (0,\infty) such that for some \widetilde{c} \in [0,\infty)^k we have

\langle\widetilde{N}^r\widetilde{\psi}\rangle \to \widetilde{c}^{\, r}

for every r\in \mathbb{N}^k as \hbar \to 0.

In particular, this implies

\langle\widetilde{N}_i\widetilde{\psi}\rangle \to \widetilde{c}_i

for every index i.

Intuitively, a semiclassical family is a family of probability distributions that becomes more and more sharply peaked with a larger and larger mean as \hbar decreases. We would like to show that in this limit, the rescaled master equation gives the rescaled rate equation.

We make this precise in the following propositions.

Proposition 1. If \widetilde{\psi} is a semiclassical family as defined above, then in the \hbar \to 0 limit, we have \langle\widetilde{N}^{\underline{r}}\widetilde{\psi}\rangle \to \widetilde{c}^{\; r} as well.

Proof. For each index i,

\displaystyle{ \langle\widetilde{N}_i^{\; \underline{r_i}}\, \widetilde{\psi}\rangle = \displaystyle{ \langle \widetilde{N}_i (\widetilde{N}_i - \hbar)(\widetilde{N}_i-2\hbar)\cdots(\widetilde{N}_i-r_i\hbar+\hbar)\,\widetilde{\psi}\rangle} }

\displaystyle{ = \Big\langle\Big(\widetilde{N}_i^r + \hbar\frac{r_i(r_i-1)}{2}\widetilde{N}_i^{r_i-1}+\cdots + \hbar^{r_i-1}(r_i-1)!\Big)\,\widetilde{\psi}\Big\rangle }

By the definition of a semiclassical family,

\displaystyle{ \lim_{\hbar\to 0} \langle\Big(\widetilde{N}_i^{r_i} + \hbar\frac{r_i(r_i-1)}{2}\widetilde{N}_i^{r_i-1}+ \cdots + \hbar^{r_i-1}(r_i-1)!\Big)\;\widetilde{\psi}\rangle} = \widetilde{c}_i^{\; r_i}

since every term but the first approaches zero. Thus, we have

\displaystyle{ \lim_{\hbar \to 0} \langle\widetilde{N}_i^{\; \underline{r_i}}\, \widetilde{\psi}\rangle =   \widetilde{c}_i^{\; r_i} }

A similar but more elaborate calculation shows that

\displaystyle{ \lim_{\hbar \to 0} \langle\widetilde{N}_1^{\, \underline{r_1}} \cdots\widetilde{N}_k^{\, \underline{r_k}} \, \widetilde{\psi}\rangle = \lim_{\hbar \to 0} \langle\widetilde{N}_1^{\, r_1}\cdots \widetilde{N}_k^{\, r_k} \widetilde{\psi}\rangle= \lim_{\hbar \to 0}\langle\widetilde{N}^{\, r} \, \widetilde{\psi}\rangle }

or in other words

\langle\widetilde{N}^{\, \underline{r}}\,\widetilde{\psi}\rangle \to \widetilde{c}^{\, r}

as \hbar \to 0.   █

Proposition 2. If \widetilde{\psi} is a semiclassical family of states, then

\displaystyle{  \langle (\widetilde{N}-\widetilde{c})^{r}\, \widetilde{\psi}\rangle \to 0 }

for any multi-index r.

Proof. Consider the r_i\mathrm{th} centered moment of the i\mathrm{th} number operator:

\displaystyle{\langle(\widetilde{N}_i-\widetilde{c}_i)^{r_i}\widetilde{\psi}\rangle = \sum_{p =0}^{r_i} {r_i \choose p}\langle\widetilde{N}_i^p\widetilde{\psi}\rangle(-\widetilde{c}_i)^{r_i-p} }

Taking the limit as \hbar goes to zero, this becomes

\begin{array}{ccl} \displaystyle{ \lim_{\hbar \to 0}\sum_{p =0}^{r_i} {r_i \choose p}\langle\widetilde{N}_i^p\widetilde{\psi}\rangle(-\widetilde{c}_i)^{r_i-p} } &=& \displaystyle{ \sum_{p =0}^{r_i} {r_i \choose p}(\widetilde{c}_i)^p(-\widetilde{c}_i)^{r_i-p} } \\ \\  &=& (\widetilde{c}_i-\widetilde{c}_i)^{r_i} \\ \\  &=& 0 \end{array}

For a general multi-index r we can prove the same sort of thing with a more elaborate calculation. First note that

\langle (\widetilde{N}-\widetilde{c})^{r}\widetilde{\psi}\rangle=\langle(\widetilde{N_1}-\widetilde{c_1})^{r_1} \cdots (\widetilde{N_k}-\widetilde{c_k})^{r_k})\widetilde{\psi}\rangle

The right-hand side can be expanded as

\displaystyle{ \langle(\sum_{p_1 =0}^{r_1} {r_1 \choose p_1}\widetilde{N}_1^{p_1}(-\widetilde{c}_1)^{r_1-p_1} ) \cdots (\sum_{p_k =0}^{r_k} {r_k \choose p_k}\widetilde{N}_k^{p_k}(-\widetilde{c}_k)^{r_k-p_k} )\widetilde{\psi} }\rangle

We can write this more tersely as

\displaystyle{ \sum_{p=0}^r} {r\choose p} \langle \widetilde{N}^p\widetilde{\psi}\rangle (-\widetilde{c})^{r-p}

where for any multi-index r we define

\displaystyle{{\sum_{p=0}^{r}}= \sum_{p_1 =0}^{r_1} \cdots  \sum_{p_k =0}^{r_k}  }

and for any multi-indices r, p we define

\displaystyle{ {r \choose p}={r_1 \choose p_1}{r_2 \choose p_2}\cdots {r_k \choose p_k}}

Now using the definition of a semiclassical state, we see

\displaystyle{ \lim_{\hbar \to 0} \sum_{p=0}^r} {r\choose p} \langle \widetilde{N}^p\widetilde{\psi}\rangle (-\widetilde{c})^{r-p}= \displaystyle{ \sum_{p=0}^r} {r\choose p} (\widetilde{c})^{p} (-\widetilde{c})^{r-p}

But this equals zero, as the last expression, expanded, is

\displaystyle{ (\widetilde{c})^r \left( \sum_{p_1=0}^{r_1} {r_1\choose p_1} (-1)^{r_1-p_1}\right) \cdots \left( \sum_{p_k=0}^{r_k} {r_k\choose p_k} (-1)^{r_k-p_k} \right) }

where each individual sum is zero.   █

Here is the theorem that would finish the job if we could give a fully rigorous proof:

“Theorem.” If \widetilde{\psi}(t) is a solution of the rescaled master equation and also a semiclassical family for the time interval [t_0,t_1], then \widetilde{x}(t) = \langle \widetilde{N} \widetilde{\psi}(t) \rangle is a solution of the rescaled rate equation for t \in [t_0,t_1].

Proof sketch. We sketch a proof that relies on the assumption that we can pass the \hbar \to 0 limit through a time derivative. Of course, to make this rigorous, we would need to justify this. Perhaps it is true only in certain cases.

Assuming that we can pass the limit through the derivative:

\displaystyle{\lim_{\hbar \to 0}\frac{d}{dt} \langle \widetilde{N}\widetilde{\psi}(t)\rangle = \lim_{\hbar \to 0} \sum_{\tau \in T} \widetilde{r}(\tau) (t(\tau)-s(\tau))\langle \widetilde{N}^{\, \underline{s(\tau)}} \, \widetilde{\psi}(t)\rangle }

and thus

\displaystyle{\frac{d}{dt}\lim_{\hbar \to 0} \langle \widetilde{N}\widetilde{\psi}(t)\rangle = \sum_{\tau \in T} \widetilde{r}(\tau) (t(\tau)-s(\tau)) \lim_{\hbar \to 0}\langle \widetilde{N}^{\, \underline{s(\tau)}} \, \widetilde{\psi}(t)\rangle }

and thus

\displaystyle{\frac{d}{dt}\widetilde{x}(t) = \sum_{\tau \in T} \widetilde{r}(\tau) (t(\tau)-s(\tau))\widetilde{x}^{\, s(\tau)} }.

As expected, we obtain the rescaled rate equation.   █

Another question is this: if we start with a semiclassical family of states as our initial data, does it remain semiclassical as we evolve it in time? This will probably be true only in certain cases.

An example: rescaled coherent states

The best-behaved semiclassical states are the coherent states.
Consider the family of coherent states

\displaystyle{\widetilde{\psi}_{\widetilde{c}} = \frac{e^{(\widetilde{c}/\hbar) z}}{e^{\widetilde{c}/\hbar}}}

using the notation developed in the earlier mentioned paper. In that paper it was shown that for any multi-index m and any coherent state \Psi we have

\langle N^{\underline{m}}\Psi\rangle = \langle N\Psi \rangle^m

Using this result for \widetilde{\psi}_{\widetilde{c}} we get

\displaystyle{\langle \widetilde{N}^{\underline{m}}\widetilde{\psi}_{\widetilde{c}}\rangle~=~\hbar^{|m|}\langle N^{\underline{m}}\widetilde{\psi}_{\widetilde{c}}\rangle~=~\hbar^{|m|}\langle N\widetilde{\psi}_{\widetilde{c}}\rangle^m~=~\hbar^{|m|}\frac{\widetilde{c}^m}{\hbar^{|m|}}~=~\widetilde{c}^m}

Since \langle\widetilde{N}^{\underline{m}}\widetilde{\psi}_{\widetilde{c}}\rangle equals \langle \widetilde{N}^{m} \widetilde{\psi}_{\widetilde{c}}\rangle plus terms of order \hbar, as \hbar \to 0 we have


showing that our chosen \widetilde{\psi}_{\widetilde{c}} is indeed a semiclassical family.

Exploring Climate Data (Part 1)

1 August, 2014

joint with Dara O Shayda

Emboldened by our experiments in El Niño analysis and prediction, people in the Azimuth Code Project have been starting to analyze weather and climate data. A lot of this work is exploratory, with no big conclusions. But it’s still interesting! So, let’s try some blog articles where we present this work.

This one will be about the air pressure on the island of Tahiti and in a city called Darwin in Australia: how they’re correlated, and how each one varies. This article will also be a quick introduction to some basic statistics, as well as ‘continuous wavelet transforms’.

Darwin, Tahiti and El Niños

The El Niño Southern Oscillation is often studied using the air pressure in Darwin, Australia versus the air pressure in Tahiti. When there’s an El Niño, it gets stormy in the eastern Pacific so the air temperatures tend to be lower in Tahiti and higher in Darwin. When there’s a La Niña, it’s the other way around:

The Southern Oscillation Index or SOI is a normalized version of the monthly mean air pressure anomaly in Tahiti minus that in Darwin. Here anomaly means we subtract off the mean, and normalized means that we divide by the standard deviation.

So, the SOI tends to be negative when there’s an El Niño. On the other hand, when there’s an El Niño the Niño 3.4 index tends to be positive—this says it’s hotter than usual in a certain patch of the Pacific.

Here you can see how this works:

When the Niño 3.4 index is positive, the SOI tends to be negative, and vice versa!

It might be fun to explore precisely how well correlated they are. You can get the data to do that by clicking on the links above.

But here’s another question: how similar are the air pressure anomalies in Darwin and in Tahiti? Do we really need to take their difference, or are they so strongly anticorrelated that either one would be enough to detect an El Niño?

You can get the data to answer such questions here:

Southern Oscillation Index based upon annual standardization, Climate Analysis Section, NCAR/UCAR. This includes links to monthly sea level pressure anomalies in Darwin and Tahiti, in either ASCII format (click the second two links) or netCDF format (click the first one and read the explanation).

In fact this website has some nice graphs already made, which I might as well show you! Here’s the SOI and also the sum of the air pressure anomalies in Darwin and Tahiti, normalized in some way:

(Click to enlarge.)

If the sum were zero, the air pressure anomalies in Darwin and Tahiti would contain the same information and life would be simple. But it’s not!

How similar in character are the air pressure anomalies in Darwin and Tahiti? There are many ways to study this question. Dara tackled it by taking the air pressure anomaly data from 1866 to 2012 and computing some ‘continuous wavelet transforms’ of these air pressure anomalies. This is a good excuse for explaining how a continuous wavelet transform works.

Very basic statistics

It helps to start with some very basic statistics. Suppose you have a list of numbers

x = (x_1, \dots, x_n)

You probably know how to take their mean, or average. People often write this with angle brackets:

\displaystyle{ \langle x \rangle = \frac{1}{n} \sum_{i = 1}^n x_i }

You can also calculate the mean of their squares:

\displaystyle{  \langle x^2 \rangle = \frac{1}{n} \sum_{i = 1}^n x_i^2 }

If you were naive you might think \langle x^2 \rangle = \langle x \rangle^2, but in fact we have:

\langle x^2 \rangle \ge \langle x \rangle^2

and they’re equal only if all the x_i are the same. The point is that if the numbers x_i are spread out, the squares of the big ones (positive or negative) contribute more to the average of the squares than if we had averaged them out before squaring. The difference

\langle x^2 \rangle - \langle x \rangle^2

is called the variance; it says how spread out our numbers are. The square root of the variance is the standard deviation:

\sigma_x = \sqrt{\langle x^2 \rangle - \langle x \rangle^2 }

and this has the slight advantage that if you multiply all the numbers x_i by some constant c, the standard deviation gets multiplied by |c|. (The variance gets multiplied by c^2.)

We can generalize the variance to a situation where we have two lists of numbers:

x = (x_1, \dots, x_n)

y = (y_1, \dots, y_n)

Namely, we can form the covariance

\langle x y \rangle - \langle x \rangle \langle y \rangle

This reduces to the variance when x = y. It measures how much x and y vary together — ‘hand in hand’, as it were. A bit more precisely: if x_i is greater than its mean value mainly for i such that y_i is greater than its mean value, the covariance is positive. On the other hand, if x_i tends to be greater than average when y_i is smaller than average — like with the air pressures at Darwin and Tahiti — the covariance will be negative.

For example, if

x = (1,-1), \quad y = (1,-1)

then they ‘vary hand in hand’, and the covariance

\langle x y \rangle - \langle x \rangle \langle y \rangle = 1 - 0 = 1

is positive. But if

x = (1,-1), \quad y = (-1,1)

then one is positive when the other is negative, so the covariance

\langle x y \rangle - \langle x \rangle \langle y \rangle = -1 - 0 = -1

is negative.

Of course the covariance will get bigger if we multiply both x and y by some big number. If we don’t want this effect, we can normalize the covariance and get the correlation:

\displaystyle{ \frac{ \langle x y \rangle - \langle x \rangle \langle y \rangle }{\sigma_x \sigma_y} }

which will always be between -1 and 1.

For example, if we compute the correlation between the air pressure anomalies at Darwin and Tahiti, measured monthly from 1866 to 2012, we get
-0.253727. This indicates that when one goes up, the other tends to go down. But since we’re not getting -1, it means they’re not completely locked into a linear relationship where one is some negative number times the other.

Okay, we’re almost ready for continuous wavelet transforms! Here is the main thing we need to know. If the mean of either x or y is zero, the formula for covariance simplifies a lot, to

\displaystyle{  \langle x y \rangle = \frac{1}{n} \sum_{i = 1}^n x_i y_i }

So, this quantity says how much the numbers x_i ‘vary hand in hand’ with the numbers y_i, in the special case when one (or both) has mean zero.

We can do something similar if x, y : \mathbb{R} \to \mathbb{R} are functions of time defined for all real numbers t. The sum becomes an integral, and we have to give up on dividing by n. We get:

\displaystyle{  \int_{-\infty}^\infty x(t) y(t)\; d t }

This is called the inner product of the functions x and y, and often it’s written \langle x, y \rangle, but it’s a lot like the covariance.

Continuous wavelet transforms

What are continuous wavelet transforms, and why should we care?

People have lots of tricks for studying ‘signals’, like series of numbers x_i or functions x : \mathbb{R} \to \mathbb{R}. One method is to ‘transform’ the signal in a way that reveals useful information. The Fourier transform decomposes a signal into sines and cosines of different frequencies. This lets us see how much power the signal has at different frequencies, but it doesn’t reveal how the power at different frequencies changes with time. For that we should use something else, like the Gabor transform explained by Blake Pollard in a previous post.

Sines and cosines are great, but we might want to look for other patterns in a signal. A ‘continuous wavelet transform’ lets us scan a signal for appearances of a given pattern at different times and also at different time scales: a pattern could go by quickly, or in a stretched out slow way.

To implement the continuous wavelet transform, we need a signal and a pattern to look for. The signal could be a function x : \mathbb{R} \to \mathbb{R}. The pattern would then be another function y: \mathbb{R} \to \mathbb{R}, usually called a wavelet.

Here’s an example of a wavelet:

If we’re in a relaxed mood, we could call any function that looks like a bump with wiggles in it a wavelet. There are lots of famous wavelets, but this particular one is the fourth derivative of a certain Gaussian. Mathematica calls this particular wavelet DGaussianWavelet[4], and you can look up the formula under ‘Details’ on their webpage.

However, the exact formula doesn’t matter at all now! If we call this wavelet y, all that matters is that it’s a bump with wiggles on it, and that its mean value is 0, or more precisely:

\displaystyle{ \int_{-\infty}^\infty y(t) \; d t = 0 }

As we saw in the last section, this fact lets us take our function x and the wavelet y and see how much they ‘vary hand it hand’ simply by computing their inner product:

\displaystyle{ \langle x , y \rangle = \int_{-\infty}^\infty x(t) y(t)\; d t }

Loosely speaking, this measures the ‘amount of y-shaped wiggle in the function x’. It’s amazing how hard it is to say something in plain English that perfectly captures the meaning of a simple formula like the above one—so take the quoted phrase with a huge grain of salt. But it gives a rough intuition.

Our wavelet y happens to be centered at t  = 0. However, we might be interested in y-shaped wiggles that are centered not at zero but at some other number s. We could detect these by shifting the function y before taking its inner product with x:

\displaystyle{ \int_{-\infty}^\infty x(t) y(t-s)\; d t }

We could also be interested in measuring the amount of some stretched-out or squashed version of a y-shaped wiggle in the function x. Again we could do this by changing y before taking its inner product with x:

\displaystyle{ \int_{-\infty}^\infty x(t) \; y\left(\frac{t}{P}\right) \; d t }

When P is big, we get a stretched-out version of y. People sometimes call P the period, since the period of the wiggles in y will be proportional to this (though usually not equal to it).

Finally, we can combine these ideas, and compute

\displaystyle{ \int_{-\infty}^\infty x(t) \; y\left(\frac{t- s}{P}\right)\; dt }

This is a function of the shift s and period P which says how much of the s-shifted, P-stretched wavelet y is lurking in the function x. It’s a version of the continuous wavelet transform!

Mathematica implements this idea for time series, meaning lists of numbers x = (x_1,\dots,x_n) instead of functions x : \mathbb{R} \to \mathbb{R}. The idea is that we think of the numbers as samples of a function x:

x_1 = x(\Delta t)

x_2 = x(2 \Delta t)

and so on, where \Delta t is some time step, and replace the integral above by a suitable sum. Mathematica has a function ContinuousWaveletTransform that does this, giving

\displaystyle{  w(s,P) = \frac{1}{\sqrt{P}} \sum_{i = 1}^n x_i \; y\left(\frac{i \Delta t - s}{P}\right) }

The factor of 1/\sqrt{P} in front is a useful extra trick: it’s the right way to compensate for the fact that when you stretch out out your wavelet y by a factor of P, it gets bigger. So, when we’re doing integrals, we should define the continuous wavelet transform of y by:

\displaystyle{ w(s,P) = \frac{1}{\sqrt{P}} \int_{-\infty}^\infty x(t) y(\frac{t- s}{P})\; dt }

The results

Dara Shayda started with the air pressure anomaly at Darwin and Tahiti, measured monthly from 1866 to 2012. Taking DGaussianWavelet[4] as his wavelet, he computed the continuous wavelet transform w(s,P) as above. To show us the answer, he created a scalogram:

This is a 2-dimensional color plot showing roughly how big the continuous wavelet transform w(s,P) is for different shifts s and periods P. Blue means it’s very small, green means it’s bigger, yellow means even bigger and red means very large.

Tahiti gave this:

You’ll notice that the patterns at Darwin and Tahiti are similar in character, but notably different in detail. For example, the red spots, where our chosen wavelet shows up strongly with period of order ~100 months, occur at different times.

Puzzle 1. What is the meaning of the ‘spikes’ in these scalograms? What sort of signal would give a spike of this sort?

Puzzle 2. Do a Gabor transform, also known as a ‘windowed Fourier transform’, of the same data. Blake Pollard explained the Gabor transform in his article Milankovitch vs the Ice Ages. This is a way to see how much a signal wiggles at a given frequency at a given time: we multiply the signal by a shifted Gaussian and then takes its Fourier transform.

Puzzle 3. Read about continuous wavelet transforms. If we want to reconstruct our signal x from its continuous wavelet transform, why should we use a wavelet y with

\displaystyle{\int_{-\infty}^\infty y(t) \; d t = 0 ? }

In fact we want a somewhat stronger condition, which is implied by the above equation when the Fourier transform of y is smooth and integrable:

Continuous wavelet transform, Wikipedia.

Another way to understand correlations

David Tweed mentioned another approach from signal processing to understanding the quantity

\displaystyle{  \langle x y \rangle = \frac{1}{n} \sum_{i = 1}^n x_i y_i }

If we’ve got two lists of data x and y that we want to compare to see if they behave similarly, the first thing we ought to do is multiplicatively scale each one so they’re of comparable magnitude. There are various possibilities for assigning a scale, but a reasonable one is to ensure they have equal ‘energy’

\displaystyle{  \sum_{i=1}^n x_i^2 = \sum_{i=1}^n y_i^2 }

(This can be achieved by dividing each list by its standard deviation, which is equivalent to what was done in the main derivation above.) Once we’ve done that then it’s clear that looking at

\displaystyle{  \sum_{i=1}^n (x_i-y_i)^2 }

gives small values when they have a very good match and progressively bigger values as they become less similar. Observe that

\begin{array}{ccl}  \displaystyle{\sum_{i=1}^n (x_i-y_i)^2 }  &=& \displaystyle{ \sum_{i=1}^n (x_i^2 - 2 x_i y_i + y_i^2) }\\  &=& \displaystyle{ \sum_{i=1}^n x_i^2 - 2 \sum_{i=1}^n x_i y_i + \sum_{i=1}^n y_i^2 }  \end{array}

Since we’ve scaled things so that \sum_{i=1}^n x_i^2 and \sum_{i=1}^n y_i^2 are constants, we can see that when \sum_{i=1}^n x_i y_i becomes bigger,

\displaystyle{ \sum_{i=1}^n (x_i-y_i)^2 }

becomes smaller. So,

\displaystyle{\sum_{i=1}^n x_i y_i}

serves as a measure of how close the lists are, under these assumptions.

The Stochastic Resonance Program (Part 1)

10 May, 2014

guest post by David Tanzer

At the Azimuth Code Project, we are aiming to produce educational software that is relevant to the Earth sciences and the study of climate. Our present software takes the form of interactive web pages, which allow you to experiment with the parameters of models and view their outputs. But to fully understand the meaning of a program, we need to know about the concepts and theories that inform it. So we will be writing articles to explain both the programs themselves and the math and science behind them.

In this two-part series, I’ll explain this program:

Stochastic resonance.

Check it out—it runs on your browser! It was created by Allan Erskine and Glyn Adgie. In the Azimuth blog article Increasing the Signal-to-Noise Ratio with More Noise, Glyn Adgie and Tim van Beek give a nice explanation of the idea of stochastic resonance, which includes some clear and exciting graphs.

My goal today is give a compact, developer-oriented introduction to stochastic resonance, which will set the context for the next blog article, where I’ll dissect the program itself. By way of introduction, I am a software developer with research training in computer science. It’s a new area for me, and any clarifications will be welcome!

The concept of stochastic resonance

Stochastic resonance is a phenomenon, occurring under certain circumstances, in which a noise source may amplify the effect of a weak signal. This concept was used in an early hypothesis about the timing of ice-age cycles, and has since been applied to a wide range of phenomena, including neuronal detection mechanisms and patterns of traffic congestion.

Suppose we have a signal detector whose internal, analog state is driven by an input signal, and suppose the analog states are partitioned into two regions, called “on” and “off” — this is a digital state, abstracted from the analog state. With a light switch, we could take the force as the input signal, the angle as the analog state, and the up/down classification of the angle as the digital state.

Consider the effect of a periodic input signal on the digital state. Suppose the wave amplitude is not large enough to change the digital state, yet large enough to drive the analog state close to the digital state boundary. Then, a bit of random noise, occurring near the peak of an input cycle, may “tap” the system over to the other digital state. So we will see a probability of state-transitions that is synchronized with the input signal. In a complex way, the noise has amplified the input signal.

But it’s a pretty funky amplifier! Here is a picture from the Azimuth library article on stochastic resonance:

Stochastic resonance has been found in the signal detection mechanisms of neurons. There are, for example, cells in the tails of crayfish that are tuned to low-frequency signals in the water caused by predator motions. These signals are too weak to cross the firing threshold for the neurons, but with the right amount of noise, they do trigger the neurons.


Stochastic resonance, Azimuth Library.

Stochastic resonance in neurobiology, David Lyttle.

Bistable stochastic resonance and Milankovitch theories of ice-age cycles

Stochastic resonance was originally formulated in terms of systems that are bistable — where each digital state is the basin of attraction of a stable equilibrium.

An early application of stochastic resonance was to a hypothesis, within the framework of bistable climate dynamics, about the timing of the ice-age cycles. Although it has not been confirmed, it remains of interest (1) historically, (2) because the timing of ice-age cycles remains an open problem, and (3) because the Milankovitch hypothesis upon which it rests is an active part of the current research.

In the bistable model, the climate states are a cold, “snowball” Earth and a hot, iceless Earth. The snowball Earth is stable because it is white, and hence reflects solar energy, which keeps it frozen. The iceless Earth is stable because it is dark, and hence absorbs solar energy, which keeps it melted.

The Milankovitch hypothesis states that the drivers of climate state change are long-duration cycles in the insolation — the solar energy received in the northern latitudes — caused by periodic changes in the Earth’s orbital parameters. The north is significant because that is where the glaciers are concentrated, and so a sufficient “pulse” in northern temperatures could initiate a state change.

Three relevant astronomical cycles have been identified:

• Changing of the eccentricity of the Earth’s elliptical orbit, with a period of 100 kiloyears

• Changing of the obliquity (tilt) of the Earth’s axis, with a period of 41 kiloyears

• Precession (swiveling) of the Earth’s axis, with a period of 23 kiloyears

In the stochastic resonance hypothesis, the Milankovitch signal is amplified by random events to produce climate state changes. In more recent Milankovitch theories, a deterministic forcing mechanism is used. In a theory by Didier Paillard, the climate is modeled with three states, called interglacial, mild glacial and full glacial, and the state changes depend on the volume of ice as well as the insolation.


Milankovitch cycle, Azimuth Library.

Mathematics of the environment (part 10), John Baez. This gives an exposition of Paillard’s theory.

Bistable systems defined by a potential function

Any smooth function with two local minima can be used to define a bistable system. For instance, consider the function V(x) = x^4/4 - x^2/2:

To define the bistable system, construct a differential equation where the time derivative of x is set to the negative of the derivative of the potential at x:

dx/dt = -V'(x) = -x^3 + x = x(1 - x^2)

So, for instance, where the potential graph is sloping upward as x increases, -V'(x) is negative, and this sends X(t) ‘downhill’ towards the minimum.

The roots of V'(x) yield stable equilibria at 1 and -1, and an unstable equilibrium at 0. The latter separates the basins of attraction for the stable equilibria.

Discrete stochastic resonance

Now let’s look at a discrete-time model which exhibits stochastic resonance. This is the model used in the Azimuth demo program.

We construct the discrete-time derivative, using the potential function, a sampled sine wave, and a normally distributed random number:

\Delta X_t = -V'(X_t) * \Delta t + \mathrm{Wave}(t) + \mathrm{Noise}(t) =
X_t (1 - X_t^2) \Delta t + \alpha * \sin(\omega t) + \beta * \mathrm{GaussianSample}(t)

where \Delta t is a constant and t is restricted to multiples of \Delta t.

This equation is the discrete-time counterpart to a continuous-time stochastic differential equation.

Next time, we will look into the Azimuth demo program itself.

Noether’s Theorem: Quantum vs Stochastic

3 May, 2014

guest post by Ville Bergholm

In 1915 Emmy Noether discovered an important connection between the symmetries of a system and its conserved quantities. Her result has become a staple of modern physics and is known as Noether’s theorem.

Photo of Emmy Noether

The theorem and its generalizations have found particularly wide use in quantum theory. Those of you following the Network Theory series here on Azimuth might recall Part 11 where John Baez and Brendan Fong proved a version of Noether’s theorem for stochastic systems. Their result is now published here:

• John Baez and Brendan Fong, A Noether theorem for stochastic mechanics, J. Math. Phys. 54:013301 (2013).

One goal of the network theory series here on Azimuth has been to merge ideas appearing in quantum theory with other disciplines. John and Brendan proved their stochastic version of Noether’s theorem by exploiting ‘stochastic mechanics’ which was formulated in the network theory series to mathematically resemble quantum theory. Their result, which we will outline below, was different than what would be expected in quantum theory, so it is interesting to try to figure out why.

Recently Jacob Biamonte, Mauro Faccin and myself have been working to try to get to the bottom of these differences. What we’ve done is prove a version of Noether’s theorem for Dirichlet operators. As you may recall from Parts 16 and 20 of the network theory series, these are the operators that generate both stochastic and quantum processes. In the language of the series, they lie in the intersection of stochastic and quantum mechanics. So, they are a subclass of the infinitesimal stochastic operators considered in John and Brendan’s work.

The extra structure of Dirichlet operators—compared with the wider class of infinitesimal stochastic operators—provided a handle for us to dig a little deeper into understanding the intersection of these two theories. By the end of this article, astute readers will be able to prove that Dirichlet operators generate doubly stochastic processes.

Before we get into the details of our proof, let’s recall first how conservation laws work in quantum mechanics, and then contrast this with what John and Brendan discovered for stochastic systems. (For a more detailed comparison between the stochastic and quantum versions of the theorem, see Part 13 of the network theory series.)

The quantum case

I’ll assume you’re familiar with quantum theory, but let’s start with a few reminders.

In standard quantum theory, when we have a closed system with n states, the unitary time evolution of a state |\psi(t)\rangle is generated by a self-adjoint n \times n matrix H called the Hamiltonian. In other words, |\psi(t)\rangle satisfies Schrödinger’s equation:

i \hbar \displaystyle{\frac{d}{d t}} |\psi(t) \rangle = H |\psi(t) \rangle.

The state of a system starting off at time zero in the state |\psi_0 \rangle and evolving for a time t is then given by

|\psi(t) \rangle = e^{-i t H}|\psi_0 \rangle.

The observable properties of a quantum system are associated with self-adjoint operators. In the state |\psi \rangle, the expected value of the observable associated to a self-adjoint operator O is

\langle O \rangle_{\psi} = \langle \psi | O | \psi \rangle

This expected value is constant in time for all states if and only if O commutes with the Hamiltonian H:

[O, H] = 0 \quad \iff \quad \displaystyle{\frac{d}{d t}} \langle O \rangle_{\psi(t)} = 0 \quad \forall \: |\psi_0 \rangle, \forall t.

In this case we say O is a ‘conserved quantity’. The fact that we have two equivalent conditions for this is a quantum version of Noether’s theorem!

The stochastic case

In stochastic mechanics, the story changes a bit. Now a state |\psi(t)\rangle is a probability distribution: a vector with n nonnegative components that sum to 1. Schrödinger’s equation gets replaced by the master equation:

\displaystyle{\frac{d}{d t}} |\psi(t) \rangle = H |\psi(t) \rangle

If we start with a probability distribution |\psi_0 \rangle at time zero and evolve it according to this equation, at any later time have

|\psi(t)\rangle = e^{t H} |\psi_0 \rangle.

We want this always be a probability distribution. To ensure that this is so, the Hamiltonian H must be infinitesimal stochastic: that is, a real-valued n \times n matrix where the off-diagonal entries are nonnegative and the entries of each column sum to zero. It no longer needs to be self-adjoint!

When H is infinitesimal stochastic, the operators e^{t H} map the set of probability distributions to itself whenever t \ge 0, and we call this family of operators a continuous-time Markov process, or more precisely a Markov semigroup.

In stochastic mechanics, we say an observable O is a real diagonal n \times n matrix, and its expected value is given by

\langle O\rangle_{\psi} = \langle \hat{O} | \psi \rangle

where \hat{O} is the vector built from the diagonal entries of O. More concretely,

\langle O\rangle_{\psi} = \displaystyle{ \sum_i O_{i i} \psi_i }

where \psi_i is the ith component of the vector |\psi\rangle.

Here is a version of Noether’s theorem for stochastic mechanics:

Noether’s Theorem for Markov Processes (Baez–Fong). Suppose H is an infinitesimal stochastic operator and O is an observable. Then

[O,H] =0

if and only if

\displaystyle{\frac{d}{d t}} \langle O \rangle_{\psi(t)} = 0


\displaystyle{\frac{d}{d t}}\langle O^2 \rangle_{\psi(t)} = 0

for all t \ge 0 and all \psi(t) obeying the master equation.   █

So, just as in quantum mechanics, whenever [O,H]=0 the expected value of O will be conserved:

\displaystyle{\frac{d}{d t}} \langle O\rangle_{\psi(t)} = 0

for any \psi_0 and all t \ge 0. However, John and Brendan saw that—unlike in quantum mechanics—you need more than just the expectation value of the observable O to be constant to obtain the equation [O,H]=0. You really need both

\displaystyle{\frac{d}{d t}} \langle O\rangle_{\psi(t)} = 0

together with

\displaystyle{\frac{d}{d t}} \langle O^2\rangle_{\psi(t)} = 0

for all initial data \psi_0 to be sure that [O,H]=0.

So it’s a bit subtle, but symmetries and conserved quantities have a rather different relationship than they do in quantum theory.

A Noether theorem for Dirichlet operators

But what if the infinitesimal generator of our Markov semigroup is also self-adjoint? In other words, what if H is both an infinitesimal stochastic matrix but also its own transpose: H = H^\top? Then it’s called a Dirichlet operator… and we found that in this case, we get a stochastic version of Noether’s theorem that more closely resembles the usual quantum one:

Noether’s Theorem for Dirichlet Operators. If H is a Dirichlet operator and O is an observable, then

[O, H] = 0 \quad \iff \quad \displaystyle{\frac{d}{d t}} \langle O \rangle_{\psi(t)} = 0 \quad \forall \: |\psi_0 \rangle, \forall t \ge 0

Proof. The \Rightarrow direction is easy to show, and it follows from John and Brendan’s theorem. The point is to show the \Leftarrow direction. Since H is self-adjoint, we may use a spectral decomposition:

H = \displaystyle{ \sum_k E_k |\phi_k \rangle \langle \phi_k |}

where \phi_k are an orthonormal basis of eigenvectors, and E_k are the corresponding eigenvalues. We then have:

\displaystyle{\frac{d}{d t}} \langle O \rangle_{\psi(t)} = \langle \hat{O} | H e^{t H} |\psi_0 \rangle = 0 \quad \forall \: |\psi_0 \rangle, \forall t \ge 0

\iff \quad \langle \hat{O}| H e^{t H} = 0 \quad \forall t \ge 0

\iff \quad \sum_k \langle \hat{O} | \phi_k \rangle E_k e^{t E_k} \langle \phi_k| = 0 \quad \forall t \ge 0

\iff \quad \langle \hat{O} | \phi_k \rangle E_k e^{t E_k} = 0 \quad \forall t \ge 0

\iff \quad |\hat{O} \rangle \in \mathrm{Span}\{|\phi_k \rangle \, : \; E_k = 0\} = \ker \: H,

where the third equivalence is due to the vectors |\phi_k \rangle being linearly independent. For any infinitesimal stochastic operator H the corresponding transition graph consists of m connected components iff we can reorder (permute) the states of the system such that H becomes block-diagonal with m blocks. Now it is easy to see that the kernel of H is spanned by m eigenvectors, one for each block. Since H is also symmetric, the elements of each such vector can be chosen to be ones within the block and zeros outside it. Consequently

|\hat{O} \rangle \in \ker \: H

implies that we can choose the basis of eigenvectors of O to be the vectors |\phi_k \rangle, which implies

[O, H] = 0


|\hat{O} \rangle \in \ker \, H

implies that

|\hat{O^2} \rangle \in \ker \: H \; \iff \; \cdots \; \iff \; \displaystyle{\frac{d}{d t}} \langle O^2 \rangle_{\psi(t)} = 0 \; \forall \: |\psi_0 \rangle, \forall t \ge 0,

where we have used the above sequence of equivalences backwards. Now, using John and Brendan’s original proof, we can obtain [O, H] = 0.   █

In summary, by restricting ourselves to the intersection of quantum and stochastic generators, we have found a version of Noether’s theorem for stochastic mechanics that looks formally just like the quantum version! However, this simplification comes at a cost. We find that the only observables O whose expected value remains constant with time are those of the very restricted type described above, where the observable has the same value in every state in a connected component.


Suppose we have a graph whose graph Laplacian matrix H generates a Markov semigroup as follows:

U(t) = e^{t H}

Puzzle 1. Suppose that also H = H^\top, so that H is a Dirichlet operator and hence i H generates a 1-parameter unitary group. Show that the indegree and outdegree of any node of our graph must be equal. Graphs with this property are called balanced.

Puzzle 2. Suppose that U(t) = e^{t H} is doubly stochastic Markov semigroup, meaning that for all t \ge 0 each row and each column of U(t) sums to 1:

\displaystyle{ \sum_i U(t)_{i j} = \sum_j U(t)_{i j} = 1 }

and all the matrix entries are nonnegative. Show that the Hamiltonian H obeys

\displaystyle{\sum_i H_{i j} = \sum_j H_{i j} = 0 }

and all the off-diagonal entries of H are nonnegative. Show the converse is also true.

Puzzle 3. Prove that any doubly stochastic Markov semigroup U(t) is of the form e^{t H} where H is the graph Laplacian of a balanced graph.

Puzzle 4. Let O(t) be a possibly time-dependent observable, and write \langle O(t) \rangle_{\psi(t)} for its expected value with respect to some initial state \psi_0 evolving according to the master equation. Show that

\displaystyle{ \frac{d}{d t}\langle O(t)\rangle_{\psi(t)} = \left\langle [O(t), H] \right\rangle_{\psi(t)} + \left\langle \frac{\partial O(t)}{\partial t}\right\rangle_{\psi(t)} }

This is a stochastic version of the Ehrenfest theorem.


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