A variational radial basis function approximation for diffusion processes

Michail D. Vrettas, Dan Cornford, Yuan Shen

Research output: Chapter in Book/Report/Conference proceedingConference publication

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.

Original languageEnglish
Title of host publicationESANN 2009 proceedings, 17th European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning
Pages497-502
Number of pages6
Publication statusPublished - 2009
Event17th European Symposium on Artificial Neural Networks: Advances in Computational Intelligence and Learning - Bruges, Belgium
Duration: 22 Apr 200924 Apr 2009

Conference

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

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  • Research Output

    Hybrid sampling on mutual information entropy-based clustering ensembles for optimizations

    Wang, F. Y., Yang, C., Lin, Z., Li, Y. & Yuan, Y., Mar 2010, In : Neurocomputing. 73, 7-9, p. 1457-1464 8 p.

    Research output: Contribution to journalArticle

  • Cite this

    Vrettas, M. D., Cornford, D., & Shen, Y. (2009). A variational radial basis function approximation for diffusion processes. In ESANN 2009 proceedings, 17th European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning (pp. 497-502)