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preprint · arXiv (Cornell University) · 2015

Pseudo-Marginal Slice Sampling

Iain Murray, Matthew M. Graham

Cited by 6Open access

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.

Cite this paper

APA 7

Murray, I., & Graham, M. M. (2015). Pseudo-Marginal Slice Sampling. arXiv (Cornell University). https://doi.org/10.48550/arxiv.1510.02958

MLA 9

Murray, Iain, and Matthew M. Graham. “Pseudo-Marginal Slice Sampling.” arXiv (Cornell University), 2015, https://doi.org/10.48550/arxiv.1510.02958.

Chicago

Murray, Iain, and Matthew M. Graham. 2015. “Pseudo-Marginal Slice Sampling.” arXiv (Cornell University). https://doi.org/10.48550/arxiv.1510.02958.

Harvard

Murray, I. and Graham, M.M. (2015) ‘Pseudo-Marginal Slice Sampling’, arXiv (Cornell University). Available at: https://doi.org/10.48550/arxiv.1510.02958.

BibTeX
@misc{murray2015pseudo,
  title = {{Pseudo-Marginal Slice Sampling}},
  author = {Murray, Iain and Graham, Matthew M.},
  journal = {arXiv (Cornell University)},
  year = {2015},
  doi = {10.48550/arxiv.1510.02958}
}

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