Pseudo-Marginal Slice Sampling

Iain Murray; Matthew M. Graham

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arXiv · 1510.02958

Pseudo-Marginal Slice Sampling

Iain Murray·Matthew M. Graham

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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.

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BibTeX RIS

Iain Murray, Matthew M. Graham. 2016-05-24. Pseudo-Marginal Slice Sampling. https://arxiv.org/abs/1510.02958

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