Pseudo-Marginal Slice Sampling

Abstract

Markov chain Monte Carlo (MCMC) methods asymptotically sample from complex probability distributions. The pseudo-marginal MCMC framework only requires an unbiased estimator of the unnormalized probability distribution function to construct a Markov chain. However, the resulting chains are harder to tune to a target distribution than conventional MCMC, and the types of updates available are limited. We describe a general way to clamp and update the random numbers used in a pseudo-marginal method's unbiased estimator. In this framework we can use slice sampling and other adaptive methods. We obtain more robust Markov chains, which often mix more quickly.

9 pages, 6 figures, 1 table. Version 2 includes citations to closely-related work released on arXiv since version 1

References in corpus (3)

• The pseudo-marginal approach for efficient Monte Carlo computations
• MCMC for doubly-intractable distributions
• Unbiased Bayesian Inference for Population Markov Jump Processes via Random Truncations

Cited by in corpus (6)

• The Block Pseudo-Marginal Sampler
• Physiological Gaussian Process Priors for the Hemodynamics in fMRI Analysis
• Geometric convergence of elliptical slice sampling
• A Common Derivation for Markov Chain Monte Carlo Algorithms with Tractable and Intractable Targets
• Unbiased Bayesian Inference for Population Markov Jump Processes via Random Truncations
• On MCMC for variationally sparse Gaussian processes: A pseudo-marginal approach