PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Variational Bayes for Continuous-Time Nonlinear State-Space Models
Antti Honkela, Matti Tornio and Tapani Raiko
In: NIPS*2006 Workshop on Dynamical Systems, Stochastic Processes and Bayesian Inference, 2006, Whistler, B.C., Canada.

Abstract

We present an extension of the variational Bayesian nonlinear state-space model introduced by Valpola and Karhunen in 2002 [1] for continuous-time models. The model is based on using multilayer perceptron (MLP) networks to model the nonlinearities. Moving to continuous-time requires solving a stochastic differential equation (SDE) to evaluate the predictive distribution of the states, but otherwise all computation happens as in the discrete-time case. The close connection between the methods allows utilising our new improved state inference method for both discrete-time and continuous-time modelling.

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EPrint Type:Conference or Workshop Item (Oral)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:3360
Deposited By:Tapani Raiko
Deposited On:09 February 2008