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Bayesian non-parametric hidden Markov models with applications in genomics
2010
Journal of The Royal Statistical Society Series B-statistical Methodology
We propose a flexible non-parametric specification of the emission distribution in hidden Markov models and we introduce a novel methodology for carrying out the computations. Whereas current approaches use a finite mixture model, we argue in favour of an infinite mixture model given by a mixture of Dirichlet processes. The computational framework is based on auxiliary variable representations of the Dirichlet process and consists of a forward-backward Gibbs sampling algorithm of similar
doi:10.1111/j.1467-9868.2010.00756.x
pmid:21687778
pmcid:PMC3116623
fatcat:5xdtn4m2ifdbpn35wt3ey7dhlm