Hidden Markov Models With Stick-Breaking Priors

The number of states in a hidden Markov model (HMM) is an important parameter that has a critical impact on the inferred model. Bayesian approaches to addressing this issue include the nonparametric hierarchical Dirichlet process, which does not extend to a variational Bayesian (VB) solution. We pre...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE transactions on signal processing 2009-10, Vol.57 (10), p.3905-3917
Hauptverfasser:	Paisley, J., Carin, L.
Format:	Artikel
Sprache:	eng
Schlagworte:	Applied sciences Bayesian analysis Bayesian methods Bioinformatics Conjugates Dirichlet problem Exact sciences and technology Hidden Markov models Hidden Markov models (HMM) hierarchical Bayesian modeling Information, signal and communications theory Jazz Mathematical models Maximum likelihood estimation Miscellaneous Music music analysis Parameter estimation Recommender systems Rock Sampling methods Signal processing Sparse matrices Speech Telecommunications and information theory Utilization variational Bayes (VB)
Online-Zugang:	Volltext bestellen
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!