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Pseudo-extended Markov chain Monte Carlo

Research output: Contribution to conference - Without ISBN/ISSN Conference paper

Publication date8/12/2019
Number of pages11
<mark>Original language</mark>English
EventThirty-third Conference on Neural Information Processing Systems - Vancouver Convention Center, Vancouver , Canada
Duration: 8/12/201914/12/2019


ConferenceThirty-third Conference on Neural Information Processing Systems
Abbreviated titleNeurIPS 2019


Sampling from posterior distributions using Markov chain Monte Carlo (MCMC) methods can require an exhaustive number of iterations, particularly when the posterior is multi-modal as the MCMC sampler can become trapped in a local mode for a large number of iterations. In this paper, we introduce the seudoextended MCMC method as a simple approach for improving the mixing of the MCMC sampler for multi-modal posterior distributions. The pseudo-extended method augments the state-space of the posterior using pseudo-samples as auxiliary variables. On the extended space, the modes of the posterior are connected, which allows the MCMC sampler to easily move between well-separated posterior modes. We demonstrate that the pseudo-extended approach delivers improved MCMC sampling over the Hamiltonian Monte Carlo algorithm on multi-modal posteriors, including Boltzmann machines and models with sparsity-inducing priors.