PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

State Inference in Variational Bayesian Nonlinear State-Space Models
Tapani Raiko, Matti Tornio, Antti Honkela and Juha Karhunen
In: 6th International Conference on Independent Component Analysis and Blind Source Separation (ICA 2006), 5-8 Mar 2006, Charleston, South Carolina, USA.

Abstract

Nonlinear source separation can be performed by inferring the state of a nonlinear state-space model. We study and improve the inference algorithm in the variational Bayesian blind source separation model introduced by Valpola and Karhunen in 2002. As comparison methods we use extensions of the Kalman filter that are widely used inference methods in tracking and control theory. The results in stability, speed, and accuracy favour our method especially in difficult inference problems.

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EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Natural Language Processing
Theory & Algorithms
ID Code:2209
Deposited By:Tapani Raiko
Deposited On:24 September 2006