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. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Raiko, T., Tornio, M., Honkela, A., & Karhunen, J. (2006). State inference in variational Bayesian nonlinear state-space models. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3889 LNCS, pp. 222–229). https://doi.org/10.1007/11679363_28
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