## 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”

## An Auxiliary Variable Trick for MCMC

[latexpage]I recently uploaded the paper “Parallel MCMC with Generalized Elliptical Slice Sampling” to the arXiv. I’d like to highlight one trick that we used, but first I’ll give some background. Markov chain Monte Carlo (MCMC) is a class of algorithms for generating samples from a specified probability distribution $\pi({\bf x})$ (in the continuous setting, the distribution is generally specified by its density function). Elliptical slice sampling is an MCMC algorithm that can be used to sample distributions of the form

\pi({\bf x}) \propto \mathcal N({\bf x};\boldsymbol\mu,\boldsymbol\Sigma) L({\bf x}),

where $\mathcal N({\bf x};\boldsymbol\mu,\boldsymbol\Sigma)$ is a multivariate Gaussian prior with mean $\boldsymbol\mu$ and covariance matrix $\boldsymbol\Sigma$, and $L({\bf x})$ is a likelihood function. Suppose we want to generalize this algorithm to sample from arbitrary continuous probability distributions. We could simply factor the distribution $\pi({\bf x})$ as

\pi({\bf x}) = \mathcal N({\bf x};\boldsymbol\mu,\boldsymbol\Sigma) \cdot \frac{\pi({\bf x})}{\mathcal N({\bf x};\boldsymbol\mu,\boldsymbol\Sigma)},

## A Continuous Approach to Discrete MCMC

[latexpage]Continuous problems are often simpler to solve than discrete problems. This is true in many optimization problems (for instance, linear programming versus integer linear programming). In the case of Markov chain Monte Carlo (MCMC), sampling continuous distributions has some advantages over sampling discrete distributions due to the availability of gradient information in the continuous case. The paper “Continuous Relaxations for Discrete Hamiltonian Monte Carlo” by Yichuan Zhang, Charles Sutton, Amos Storkey, and Zoubin Ghahramani explores the idea of performing inference in the discrete setting by deriving and sampling a related continuous distribution. Here I describe the approach taken in this paper. Continue reading “A Continuous Approach to Discrete MCMC”