Pseudo-marginal MCMC

[latexpage]This post gives a brief introduction to the pseudo-marginal approach to MCMC. A very nice explanation, with examples, is available here. Frequently, we are given a density function $\pi({\bf x})$, with ${\bf x} \in \mathcal X$, and we use Markov chain Monte Carlo (MCMC) to generate samples from the corresponding probability distribution. For simplicity, suppose we are performing Metropolis-Hastings with a spherical proposal distribution. Then, we move from the current state ${\bf x}$ to a proposed state ${\bf x}’$ with probability $\min(1, \pi({\bf x}’)/\pi({\bf x}))$ .

But what if we cannot evaluate $\pi({\bf x})$ exactly? Such a situation might arise if we are given a joint density function $\pi({\bf x}, {\bf z})$, with ${\bf z} \in \mathcal Z$, and we must marginalize out ${\bf z}$ in order to compute $\pi({\bf x})$. In this situation, we may only be able to approximate

\[ \pi({\bf x}) = \int \pi({\bf x},{\bf z}) \, \mathrm d{\bf z} ,\] Continue reading “Pseudo-marginal MCMC”

Asymptotic Equipartition of Markov Chains


The Asymptotic Equipartition Property/Principle (AEP) is a well-known result that is likely covered in any introductory information theory class. Nevertheless, when I first learned about it in such a course, I did not appreciate the implications of its general form.  In this post I will review this beautiful, classic result and offer the mental picture I have of its implications. I will frame my discussion in terms of Markov chains with discrete state spaces, but note that the AEP holds even more generally. My treatment will be relatively informal, and I will assume basic familiarity with Markov chains. See the references for more details.
Continue reading “Asymptotic Equipartition of Markov Chains”