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Poster
Pseudo-Extended Markov chain Monte Carlo
Christopher Nemeth · Fredrik Lindsten · Maurizio Filippone · James Hensman

Tue Dec 10 05:30 PM -- 07:30 PM (PST) @ East Exhibition Hall B + C #159
in Probabilistic Methods -- MCMC »

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 pseudo-extended 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.

Keywords: MCMC · Probabilistic Methods · Probabilistic Methods
[ Paper]  [ Poster] 

Author Information

Christopher Nemeth (Lancaster University)
Fredrik Lindsten (Linköping University)
Maurizio Filippone (EURECOM)
James Hensman (PROWLER.io)

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