Variational Bayes for Continuous-Time Nonlinear State-Space Models
نویسندگان
چکیده
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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تاریخ انتشار 2006