A variational radial basis function approximation for diffusion processes

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Abstract

In this paper we present a radial basis function based extension to a recently proposed variational algorithm for approximate inference for diffusion processes. Inference, for state and in particular (hyper-) parameters, in diffusion processes is a challenging and crucial task. We show that the new radial basis function approximation based algorithm converges to the original algorithm and has beneficial characteristics when estimating (hyper-)parameters. We validate our new approach on a nonlinear double well potential dynamical system.

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Publication date2009
Publication titleESANN 2009 proceedings, 17th European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning
Pages497-502
Number of pages6
Original languageEnglish
Event17th European Symposium on Artificial Neural Networks - Bruges, Belgium

Conference

Conference17th European Symposium on Artificial Neural Networks
Abbreviated titleESANN 2009
CountryBelgium
CityBruges
Period22/04/0924/04/09

Research outputs

Employable Graduates; Exploitable Research

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