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 Signal Separation (ICA 2006), 5-8 Mar 2006, Charleston, South Carolina, USA.
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.