Approximating a non-homogeneous HMM with Dynamic Spatial Dirichlet Process

In this work we present a model that uses a Dirichlet process (DP) with a dynamic spatial constraints to approximate a non-homogeneous hidden Markov model (NHMM). The coefficient of the spatial constraint, which is locally dependent on each site, modulates the time-variant transition probability mat...

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Bibliographische Detailangaben

Hauptverfasser:	Ren, H., Liang Wu, Neskovic, P., Cooper, L.
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	Bayesian methods Brain modeling Educational institutions Handwriting recognition Hidden Markov models Inference algorithms Physics Software engineering Speech State estimation
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