Augmented Ensemble MCMC sampling in Factorial Hidden Markov Models

Kaspar Märtens, Michalis Titsias, Christopher Yau
Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics, PMLR 89:2359-2367, 2019.

Abstract

Bayesian inference for Factorial Hidden Markov Models is challenging due to the exponentially sized latent variable space. Standard Monte Carlo samplers can have difficulties effectively exploring the posterior landscape and are often restricted to exploration around localised regions that depend on initialisation. We introduce a general purpose ensemble Markov Chain Monte Carlo (MCMC) technique to improve on existing poorly mixing samplers. This is achieved by combining parallel tempering and an auxiliary variable scheme to exchange information between the chains in an efficient way. The latter exploits a genetic algorithm within an augmented Gibbs sampler. We compare our technique with various existing samplers in a simulation study as well as in a cancer genomics application, demonstrating the improvements obtained by our augmented ensemble approach.

Cite this Paper

BibTeX
@InProceedings{pmlr-v89-martens19a, title = {Augmented Ensemble MCMC sampling in Factorial Hidden Markov Models}, author = {M\"{a}rtens, Kaspar and Titsias, Michalis and Yau, Christopher}, booktitle = {Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics}, pages = {2359--2367}, year = {2019}, editor = {Chaudhuri, Kamalika and Sugiyama, Masashi}, volume = {89}, series = {Proceedings of Machine Learning Research}, month = {16--18 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v89/martens19a/martens19a.pdf}, url = {https://proceedings.mlr.press/v89/martens19a.html}, abstract = {Bayesian inference for Factorial Hidden Markov Models is challenging due to the exponentially sized latent variable space. Standard Monte Carlo samplers can have difficulties effectively exploring the posterior landscape and are often restricted to exploration around localised regions that depend on initialisation. We introduce a general purpose ensemble Markov Chain Monte Carlo (MCMC) technique to improve on existing poorly mixing samplers. This is achieved by combining parallel tempering and an auxiliary variable scheme to exchange information between the chains in an efficient way. The latter exploits a genetic algorithm within an augmented Gibbs sampler. We compare our technique with various existing samplers in a simulation study as well as in a cancer genomics application, demonstrating the improvements obtained by our augmented ensemble approach.} }
Endnote
%0 Conference Paper %T Augmented Ensemble MCMC sampling in Factorial Hidden Markov Models %A Kaspar Märtens %A Michalis Titsias %A Christopher Yau %B Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2019 %E Kamalika Chaudhuri %E Masashi Sugiyama %F pmlr-v89-martens19a %I PMLR %P 2359--2367 %U https://proceedings.mlr.press/v89/martens19a.html %V 89 %X Bayesian inference for Factorial Hidden Markov Models is challenging due to the exponentially sized latent variable space. Standard Monte Carlo samplers can have difficulties effectively exploring the posterior landscape and are often restricted to exploration around localised regions that depend on initialisation. We introduce a general purpose ensemble Markov Chain Monte Carlo (MCMC) technique to improve on existing poorly mixing samplers. This is achieved by combining parallel tempering and an auxiliary variable scheme to exchange information between the chains in an efficient way. The latter exploits a genetic algorithm within an augmented Gibbs sampler. We compare our technique with various existing samplers in a simulation study as well as in a cancer genomics application, demonstrating the improvements obtained by our augmented ensemble approach.
APA
Märtens, K., Titsias, M. & Yau, C.. (2019). Augmented Ensemble MCMC sampling in Factorial Hidden Markov Models. Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 89:2359-2367 Available from https://proceedings.mlr.press/v89/martens19a.html.

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