2012/06/18 by Ryohei Fujimaki, Kohei Hayashi, Fujimaki, Ryohei +1 · 16 citations
Computer Science · Mathematics · #Artificial intelligence #Bayesian Methods and Mixture Models #Bayesian Modeling and Causal Inference #Bayesian inference #Bayesian probability #Computer science #Consistency (knowledge bases) #FOS: Computer and information sciences #Hidden Markov model #Hidden variable theory #Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Algorithms #Machine learning #Marginal likelihood #Mathematics #Model selection #Parametric statistics #Pattern recognition (psychology) #Selection (genetic algorithm) #Statistics #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1206.4679
published in arXiv (Cornell University) (Cornell University) · ICML2012
arxiv created 2012/06/18 · openalex publication_date 2012/06/18 · arxiv updated 2012/06/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.