Jonathan Huggins will present his joint work with Frank Wood. Here is an abstract:
We develop a class of non-parametric Bayesian models we call infinite structured explicit duration hidden Markov models (ISEDHMMs). ISEDHMMs are HMMs that possess an unbounded number of states, encode state dwell-time distributions explicitly, and have constraints on what state transitions are allowed. The ISEDHMM framework generalizes explicit duration finite HMMs, infinite HMMs, left-to-right HMMs, and more (all are recoverable by specific choices of ISEDHMM parameters). This suggests that ISEDHMMs should be applicable to data-analysis problems in a variety of settings.
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