Finite state space non parametric Hidden Markov Models are in general identifiable

2013/06/19 by Élisabeth Gassiat, Gassiat, Elisabeth, Alice Cleynen +3
Computer Science · #Bayesian Methods and Mixture Models #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Machine Learning and Algorithms #Methodology (stat.ME)

paper · pdf · doi:10.48550/arxiv.1306.4657

openalex publication_date 2013/06/19 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28

Abstract

In this paper, we prove that finite state space non parametric hidden Markov models are identifiable as soon as the transition matrix of the latent Markov chain has full rank and the emission probability distributions are linearly independent. We then propose several non parametric likelihood based estimation methods, which we apply to models used in applications. We finally show on examples that the use of non parametric modeling and estimation may improve the classification performances.

Citations

• Non parametric finite translation mixtures with dependent regime

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