Article

Factorized asymptotic Bayesian hidden Markov models

Authors:

Ryohei Fujimaki,

Kohei HayashiAuthors Info & Claims

ICML'12: Proceedings of the 29th International Coference on International Conference on Machine Learning

Pages 1203 - 1210

Published: 26 June 2012 Publication History

Abstract

This paper addresses the issue of model selection for hidden Markov models (HMMs). We generalize factorized asymptotic Bayesian inference (FAB), which has been recently developed for model selection on independent hidden variables (i.e., mixture models), for time-dependent hidden variables. As with FAB in mixture models, FAB for HMMs is derived as an iterative lower bound maximization algorithm of a factorized information criterion (FIC). It inherits, from FAB for mixture models, several desirable properties for learning HMMs, such as asymptotic consistency of FIC with marginal log-likelihood, a shrinkage effect for hidden state selection, monotonic increase of the lower FIC bound through the iterative optimization. Further, it does not have a tunable hyper-parameter, and thus its model selection process can be fully automated. Experimental results shows that FAB outperforms states-of-the-art variational Bayesian HMM and non-parametric Bayesian HMM in terms of model selection accuracy and computational efficiency.

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Cited By

Imaizumi MFujimaki R(2017)Factorized asymptotic bayesian policy search for POMDPsProceedings of the 26th International Joint Conference on Artificial Intelligence10.5555/3171837.3171894(4346-4352)Online publication date: 19-Aug-2017
https://dl.acm.org/doi/10.5555/3171837.3171894
Xu ZCheng JXiao XFujimaki RMuraoka Y(2017)Efficient nonparametric and asymptotic Bayesian model selection methods for attributed graph clusteringKnowledge and Information Systems10.1007/s10115-017-1030-853:1(239-268)Online publication date: 1-Oct-2017
https://dl.acm.org/doi/10.1007/s10115-017-1030-8

Factorized asymptotic Bayesian hidden Markov models
1. Computing methodologies
2. Mathematics of computing
  1. Probability and statistics
    1. Probabilistic reasoning algorithms
    2. Probabilistic representations

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Information

Published In

cover image Guide Proceedings

ICML'12: Proceedings of the 29th International Coference on International Conference on Machine Learning

June 2012

1912 pages

ISBN:9781450312851

Program Chairs:
John Langford
Yahoo! Research
,
Joelle Pineau
McGill University

Sponsors

PASCAL2 - Pattern Analysis, Statistical Modelling and Computational Learning
IBMR: IBM Research
NSF
Microsoft Research: Microsoft Research
Facebook: Facebook

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Omnipress

Madison, WI, United States

Publication History

Published: 26 June 2012

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Cited By

Imaizumi MFujimaki R(2017)Factorized asymptotic bayesian policy search for POMDPsProceedings of the 26th International Joint Conference on Artificial Intelligence10.5555/3171837.3171894(4346-4352)Online publication date: 19-Aug-2017
https://dl.acm.org/doi/10.5555/3171837.3171894
Xu ZCheng JXiao XFujimaki RMuraoka Y(2017)Efficient nonparametric and asymptotic Bayesian model selection methods for attributed graph clusteringKnowledge and Information Systems10.1007/s10115-017-1030-853:1(239-268)Online publication date: 1-Oct-2017
https://dl.acm.org/doi/10.1007/s10115-017-1030-8

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