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Bayesian continuous density hidden Markov model

In Section 4.1.4, a Bayesian formulation of the HMM based on the work of MacKay [39] is presented. This model is now extended for continuous observations instead of discrete ones.

Normally continuous density hidden Markov models (CDHMMs) use a mixture model like mixture-of-Gaussians as the distribution of the observations for a given state. This is, however, unnecessarily complicated for the HMM/NSSM hybrid so single Gaussians will be used as the observation model.


Antti Honkela 2001-05-30