State inference in variational bayesian nonlinear state-space models

Authors:
Tapani Raiko;Matti Tornio;Antti Honkela;Juha Karhunen
Affiliations:
Neural Networks Research Centre, Helsinki University of Technology, HUT, Espoo, Finland;Neural Networks Research Centre, Helsinki University of Technology, HUT, Espoo, Finland;Neural Networks Research Centre, Helsinki University of Technology, HUT, Espoo, Finland;Neural Networks Research Centre, Helsinki University of Technology, HUT, Espoo, Finland
Venue:
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Year:
2006

Citing 2
Cited 3

An unsupervised ensemble learning method for nonlinear dynamic state-space models

Neural Computation
Adaptive Algorithm for Blind Separation from Noisy Time-Varying Mixtures

Neural Computation

Building Blocks for Variational Bayesian Learning of Latent Variable Models

The Journal of Machine Learning Research
Variational Bayesian learning of nonlinear hidden state-space models for model predictive control

Neurocomputing
Long-Term prediction of time series using state-space models

ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II

Quantified Score

Hi-index	0.00

Visualization

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.