Inference in Hidden Markov Models with Explicit State Duration Distributions

Details Inference in Hidden Markov Models with Explicit State Duration Distributions Inference in Hidden Markov Models with Explicit State Duration Distributions

2012-02-29

arxiv.org/abs/1203.0038v1

Statistics

Machine Learning

Scientific paper

In this letter we borrow from the inference techniques developed for unbounded state-cardinality (nonparametric) variants of the HMM and use them to develop a tuning-parameter free, black-box inference procedure for Explicit-state-duration hidden Markov models (EDHMM). EDHMMs are HMMs that have latent states consisting of both discrete state-indicator and discrete state-duration random variables. In contrast to the implicit geometric state duration distribution possessed by the standard HMM, EDHMMs allow the direct parameterisation and estimation of per-state duration distributions. As most duration distributions are defined over the positive integers, truncation or other approximations are usually required to perform EDHMM inference.

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