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Article overview
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Inference in Hidden Markov Models with Explicit State Duration Distributions | Michael Dewar
; Chris Wiggins
; Frank Wood
; | Date: |
29 Feb 2012 | Abstract: | 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. | Source: | arXiv, 1203.0038 | Services: | Forum | Review | PDF | Favorites |
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