The second MC in MCMC methods

In calculus differentiating a function is easy. Integrating one is hard. Much research has gone into finding ways for calculating integrals. But often functions cannot be integrated, at least analytically.

No problem. You do it numerically. You take the function’s curve, split it up into nice rectangles or, even better, trapezoids, and calculate their areas, and add them up. Not accurate enough? Split the curve up even finer until you’re happy. Done.

That works fine in one dimension and even two dimensions. But then, as the number of dimensions increases, the amount of splitting and adding grows exponentially, soon hitting unwieldy numbers. This is a mountain too steep for even the fastest computers. The problem is what applied mathematics guru Richard E. Bellman famously called the curse of dimensionality.

But thankfully randomness offers a path around this mountain. The approach is called Monte Carlo integration for estimating sums and integrals

This is the second part of a series of posts on Mark chain Monte Carlo methods. The first part covers Markov chains. This posts covers the basics of Monte Carlo methods. The third part will combine the ideas from the first two parts. Overall, the three posts will sketch the mechanics of Markov chain Monte Carlo (MCMC) methods, whose importance and applications I detailed in a previous post.

We’ll focus on estimating sums, but the same logic applies to integrals.

Monte Carlo estimates

In the discrete (or countable) case, consider a function \(f\) that maps from a countable state space \(\mathbb{S}\). This state space maybe, for example, the lattice \(\mathbb{Z}^d\) or some subset of it. We write the sum as

$$S(f)=\sum_{x\in \mathbb{S}} f(x) $$

Such sums arise in many fields, particularly statistical physics, which is where the (Markov chain) Monte Carlo methods were born.1In general, we use integrals for continuous spaces, which are uncountable, whereas we use sums for discrete spaces, which are countable spaces. (The two are generalized by the concept of an integral measure.)

The idea behind a Monte Carlo estimate of a sum is remarkably simple. You randomly sample the function \(f\), find the average, and use it as an estimate of the sum.

Estimating with uniform variables

For a finite set \(\mathbb{S}=\{x_1,\dots,x_m\}\), we write the sum of \(f\) over the set \(\mathbb{S}\) as

$$\begin{align}S(f)&=\sum_{x\in \mathbb{S}}  f(x) \frac{m}{m} \\&= \sum_{x\in\mathbb{S}}  \frac{f(x)p(x)}{p(x)}\\& = \mathbb{E}_{p}[ \frac{f(U)}{p(U)}]\,,\end{align}$$

where \(U\) is a discrete uniform random variables, which has the probability mass function \(p\) with support \(\mathbb{S}\), meaning \(p(x)=1/m\) if \(x\in \mathbb{S}\) and \(p(x)=0\) otherwise. But using a uniform random variable is a naive way, as there are better random variables we can use to sample \(f\).

Estimating with general variables

More generally, we can write the sum of a countable (possibly infinite) set \(\mathbb{S}\) as

$$\begin{align}S(f)&= \sum_{x\in\mathbb{S}}  \frac{f(x)q(x)}{q(x)}\\& = \mathbb{E}_{q}[ \frac{f(Y)}{q(Y)}]\\& = \mathbb{E}_{q}[ g(Y)]\,,\end{align}$$

where \(g=p/q\) and \(Y\) is a suitable random variable that has a probability mass function  \(q\) with support \(\mathbb{S}\).

Sampling the random variable (or point) \(Y\), gives the samples \(y_1,\dots,y_n\), which yields an unbiased Monte Carlo estimate of the sum

$$S_n (f)=\frac{1}{n}\sum_{i=1}^n \frac{f(y_i)}{q(y_i)}.$$

If we can (quickly) sample \(Y\), then we can (quickly) estimate the integral. The closer the (probability mass) function \(q\) is to \(|f|\), the better the estimate.

We can also use the above reasoning with integrals, replacing the sum and probability mass function with an integral and a probability density.

Error rate

If the samples \(y_1, \dots, y_n \) are random and independent, the (strong) law of large numbers guarantees that the unbiased Monte Carlo estimate converges (almost surely) to the correct answer.

Further assuming \(S(f^2)<\infty\), the error of the integral estimate \(|S(f)-S_n(f)|\) is proportional to

$$\frac{\mathbb{V}_q(f)}{n ^{1/2}},
is the variance of the function \(f\). (This is not the variation of a function, which is something different.) The error \(|S(f)-S_n(f)|\) reduces at the rate \(O(n^{-1/2})\). The Monte Carlo approach allows for continual updating until the estimate is sufficiently converged.

The above logic applies to integrals of functions on general spaces.

The dimension \(d\) of the underlying space, as $\mathbb{S}^d$, does not appear in the error. The independence of the dimension is precisely why we tackle high-dimensional sums and integrals with Monte Carlo methods. The general rule of thumb is to use Monte Carlo integration for functions of four or more dimensions.

Losing the independence

Again, a sequence of independent random variables allow our Monte Carlo estimate to converge. We can loosen this requirement and allow some dependence between variables. To obtain a Monte Carlo estimate, we just need to use a sequence of random variables that are ergodic. Then we recover the (strong) law of large numbers that gives our Monte Carlo estimate.


The word ergodicity is one of those technical terms you’ll see and hear in different fields of mathematics and physics, often when randomness is involved. The concept is also central to deterministic dynamical systems.

There are different versions and strengths of ergodicity, depending on the system and applications. But loosely speaking, an ergodic system is one in which temporal averages and spatial (or ensemble) averages converge to the same value.

That means when you want to find an average, you have two options in an ergodic system. The temporal option: You can measure something over a long time. The spatial (or ensemble) option: You can measure many instances of the same something. The two different averages will eventually converge to the same value.

Ergodic sequences

In the context of Markov processes and, more generally, stochastic processes, we can think of ergodicity as convergence and a slightly more general version of the law of large numbers.

Consider a stochastic process \(X\), which is a sequence of random variables \(X_0,X_1,\dots\) indexed by some parameter \(t\). We assume the probability distributions \(\pi_0,\pi_1,\dots\) of the random variables converge to some final stationary distribution \(\pi\). For a countable state space, the (probability) distribution \(\pi\) corresponds to a probability mass function.

We then say a stochastic process \(X\) state space is ergodic if the following holds almost surely

$$ \frac{1}{t}\sum_{i=0}^t g(X_i)\rightarrow \sum_{x\in \mathbb{Z}}g(x)\pi(x)=\mathbb{E}_{\pi} [g(X)]\,,$$

as \(t\rightarrow\infty\) for all bounded functions, nonnegative functions \(g\). The subscript of the expectation simply denotes that the expectation is taken with respect to the (stationary) distribution \(\pi\).

The equation tells the story. On the left-hand side you have a temporal average. On the right-hand side you have a spatial (or ensemble) average. As time proceeds, the first average will converge to the second average. Ergodicity.

Further reading

There are many books dedicated entirely to the subject of Monte Carlo methods. For example, a good book with detailed section on Markov chains applied to Monte Carlo methods in the context of statistics is:

  • Casella and Robert, Monte Carlo Statistical Methods.

These books also have sections on Monte Carlo integration:

The first MC in MCMC methods

Markov chains form a fundamentally important class of stochastic processes. It would be hard to over stress their importance in probability, statistics, and, more broadly, science and technology. They are indispensable in random simulations, particularly those based on the Markov chain Monte Carlo methods. In this post, we’ll have a look some Markov chain basics needed for such simulation methods.

This is the first part of a series of posts on Mark chain Monte Carlo methods. This post covers the basics of Markov chains, which is the more involved part. The second part will cover Monte Carlo methods. The third part will combine the ideas from the first two parts. Overall, the three posts will sketch the mechanics of Markov chain Monte Carlo (MCMC) methods, whose importance and applications I detailed in a previous post.

Markov chains vs Markov processes

All Markov chains are Markov processes. Some people use the term Markov chain to refer to discrete-time Markov processes with general state spaces. Other people prefer the term Markov chain for continuous-time Markov processes with countable state spaces.1In his book Applied Probability and Queues Asmussen writes:

In this book, we use the terminology that a Markov chain has discrete time and a Markov process has continuous time (the state space may be discrete or general). However, one should note that it is equally common to let “chain” refer to a discrete state space and “process” to a general one (time may be discrete or continuous).

Nevertheless, the first MC in the MCMC suggests the Markov chain Monte Carlo crowd prefers the former sense of Markov chain, given the use of discrete-time Markov processes in their simulations.

Markov the frog

Some writers introduce Markov chains with a mental image of a frog jumping around lily pads scattered over a pond. (Presumably the frog never misses a lily pad.) We assume the frog randomly chooses the next lily pad through some random mechanism. Perhaps the distances between lily pads or their sizes influence the chances that frog will jump between them.

We further assume that the frog is a bit particular, preferring to jump in certain directions more than others. More precisely, the probability of our frog jumping from a lily pad labelled \(x\) to another labelled \(y\) is \(P(x,y)\). But jumping in the opposite direction happens with probability \(P(y,x)\), which in general is not equal to \(P(x,y)\).

I typically use the term points, but the Markov literature usually says that the Markov chain visits states.

State space

We can interpret a Markov chain, a type of stochastic process, as a collection or sequence of random variables. 2I briefly detailed stochastic processes in a previous post.The values of the random variables are points in some mathematical space \(\mathbb{X}\). This space can be quite abstract, but in practice it’s usually the lattice \(\mathbb{Z}^n\), Euclidean space \(\mathbb{R}^n\), or a subset of one of these two spaces. For our frog example, all the lily pads in the pond form the state space.

We’ll only consider countable Markov chains where the number of points in the state space \(\mathbb{X}\) is countable. Although the results and theory generally hold for more general state spaces, the accompanying work requires more technical mathematics. For finite and countable state spaces, we can use standard probability and matrix knowledge. But when we use uncountable state spaces such as \(\mathbb{R}^n\), we enter the world of measure theory and functional analysis.

I will often write a point \(x\) in a (state) space \(\mathbb{X}\). But you can say an element \(x\) of a set \(\mathbb{X}\). Many authors refers to the points or elements as states of the Markov chain. In the frog example, each lily pad is a different state.

Markov property

A discrete-time countable Markov chain is a random process that jumps between points of some countable mathematical space \(\mathbb{X}\) such that, when at point \(x \in \mathbb{X}\), the next position is chosen according to a probability distribution \(P(x,·)\) depending only on \(x\).

More specifically, a sequence of random variables \((X_0, X_1, . . .)\) is a discrete-time Markov chain \(X\) with a countable state space
\(\mathbb{X}\) and kernel \(P\) if for all \(x,y \in \mathbb{X}\) and all \(t \geq 1\) satisfying \(\mathbb{P}[X_{t−1}=x_{t-1},\dots,X_0=x_0]>0\), we have

$$ \begin{align}\mathbb{P}[X_{t+1} =y|&X_{t}=x,X_{t−1}=x_{t-1},\dots,X_0=x_0]\\&=\mathbb{P}[X_{t+1} =y|X_t =x]\\&=P(x,y)\,.\end{align}$$

This equation is often called the Markov property.

The Markov property says that the conditional probability of jumping from point \(x\) to \(y\) remains the same, regardless of which points or states \(x_0,x_1,\dots,x_{t-1}\) were previously visited. This is precisely why the kernel \(P\) contains all the information needed to describe the future random evolution of the Markov chain.

We have assumed the probabilities given by \(P\) are fixed, meaning we have described a homogeneous Markov chain.

Markov kernel

The kernel \(P\) is called the Markov (transition) kernel or probability kernel. Assuming a countable state space \(\mathbb{X}\), we can reference any probability value of the kernel \(P\) with two variables \(x,y\in\mathbb{X}\). If we assume a finite state space \(\mathbb{X}\), then the kernel \(P\) becomes a regular matrix taught in linear algebra. An infinite but countable state space gives an infinite matrix \(P\). The rows of the kernel matrix \(P\) must add up to one, because each row is a probability measure.

A more general space, such as Euclidean space \(\mathbb{R}^n\), results in a more general kernel with respect to a suitable measure. In this setting, \(P(x,·)\) is no longer a probability mass function, but a general probability measure.

Initial distribution

At time \(t=0\) we describe the random initial configuration of a Markov process with a probability distribution \(\mu_0\). For a finite or countable Markov chain, this initial distribution \(\mu_0\) corresponds to a probability mass function encoded as a row vector.

Jumping from \(x\) to \(y\)

The probability distribution \(\mu_0\) gives the probability of starting in state (or at point) \(x\in\mathbb{X}\). After one time step, we can write down the probability distribution \(\mu_1\) that gives us the different probabilities of the Markov chain being at different states. At \(n=1\), basic matrix algebra and probability rules give us the matrix equation

$$\mu_1=\mu_0 P$$

By induction, after \(t\) time steps we have the expression

$$\mu_n=\mu_0 P^n\,.$$

where the superscript \(n\) denotes matrix power. We can write the \(n\)-time step kernel as \(P_{(n)}\), which for a finite Markov chain is given by the matrix equation \(P_{(n)}=P^n\).

Seeing how \(P_{(n)}\) behaves as \(n\) approaches infinity forms part of work that studies the convergence and ergodicity properties of Markov chains. I’ll make these concepts clearer below. But first I’ll give some conditions that are typically needed.

Regularity conditions

A Markov chain with a countable state space needs some conditions to ensure convergence and ergodicity.

Regularity conditions

  1. A stationary distribution \(\pi\)
  2. Aperiodicity
  3. Irreducibility
  4. Postive recurrence

The nature of the state space and the kernel will dictate these conditions. These conditions are also not necessarily logically distinct. For example, on a finite state space, you’ll get positive recurrence for free, because an aperiodic, irreducible Markov chain with a finite state space is always positive recurrent.

We now briefly detail these conditions and in another post I’ll give examples how the conditions can be met.

Stationary distribution \(\pi\)

It’s possible to encounter a probability distribution \(\pi\) where applying the kernel \(P\) returns the same distribution \(\pi\), meaning

$$ \pi=\pi P\,.$$

This (fixed-point) equation is called the balance equation.

The distribution \(\pi\) is called the stationary, invariant or steady-state distribution. A Markov chain does not need to have a stationary distribution. And if a Markov chain does have one, it may not be unique. Its existence and uniqueness will depend on the Markov kernel \(P\).

Showing that a unique stationary distribution exists and it is possible to reach it with probability one is the stuff of Markov convergence results. Markov chain Monte Carlo methods hinge upon these results .


It is possible for a Markov chain to get trapped in a loop, periodically visiting the same states. The period \(d_x\) of a state \(x\in \mathbb{x}\) is the greatest common divisor of all \(n\) values such that \(P(x,x)^n>0\). If the period of a point is \(d_x=1\), then we say it’s aperiodic. If every state of a Markov chain is aperiodic, we says it’s an aperiodic Markov chain.

Aperiodicity means there are no loops to trap the Markov chain. This property is typically needed for convergence results.


A Markov chain with a countable state space \(\mathbb{X}\) is irreducible if the Markov chain can go from any point \(x\in\mathbb{X}\) to another other point \(x\in\mathbb{X}\) with a positive probability in a finite number of time steps. In other words, there exists a natural number \(s\) such that \(P(x,y)^s>0\) for all \(x,y\in\mathbb{X}\).

Irreducibility ensures that a Markov chain will visit all the states in its state space. This property is also needed for convergence results.


When studying Markov processes, a quantity of interest is how much time it takes to return to a state or point. For a point \(x\in\mathbb{X}\), we define its first return time as

$$ T_x^+=\min\{ t\geq 1: X_t=x\} \,.$$

As the name suggests, this random variable is the number of time steps for the Markov process return to state \(x\), taking whichever path, conditioned on it starting at \(x\).

We call a state \(x\) recurrent if the probability of its first return time being finite is one, meaning \(\mathbb{P}_x(T_x^+<\infty)=1\). Otherwise the state \(x\) is said to be transient.

Positive recurrence

We can classify different types of recurrence based on the expected value of the first return times. A state \(x\) is called positive recurrent if the expected value of its first return time is finite, meaning \(\mathbb{E}_x(T_x^+)<\infty\). Otherwise state \(x\) is null recurrent.

For a countable Markov chain, if all the states in the state space are (positive) recurrent, so \(\mathbb{E}_x(T_x^+)<\infty\) for all \(x\in\mathbb{X}\), then we say the Markov chain is (positive) recurrent.

Again, the concept of positive recurrence is needed for convergence results.


We say a countable Markov chain is ergodic if it is irreducible, aperiodic and positive recurrent.3See, for example, Basics of Applied Stochastic Process by Serfozo (page 26) or Probability Theory and Stochastic Processes by Bremaud (page 262). Ergodicity allows one to find averages by employing a more general form of the law of large numbers, which Monte Carlo methods rely upon. We stress that definitions of ergodicity vary somewhat, but in general it means convergence and laws of large numbers exists.

Further reading

Any stochastic process book will include a couple of chapters on Markov chains such as:

    • Brémaud – Probability Theory and Stochastic Processes;
    • Serfozo -Basics of Applied Stochastic Processes.

For more details, there are many books dedicated entirely to the subject of Markov chains. For example, introductory books include:

  • Brémaud – Markov Chains, Gibbs Fields, Monte Carlo Simulation and Queues;
  • Levin, Peres, and Wilmer – Markov Chains and Mixing Times;
  • Norris – Markov Chains.

Those books cover Markov chains with countable state spaces. If you want to read about discrete-time Markov chains with general state spaces, try the book:

  • Meyn, Tweedie – Markov chains and stochastic stability

All the above books have a section on Markov chain Monte Carlo methods, such as the Metropolis-Hastings algorithm or the Gibbs sampler.