A new variational radial basis function approximation for inference in multivariate diffusions

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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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  • Vrettas2009ESANN.pdf

    Rights statement: Advances in Computational Intelligence and Learning - 17th European Symposium on Artificial Neural Networks 2009, Bruges (BE), 22-24 April 2009. NOTICE: this is the author’s version of a work that was accepted for publication in Neurocomputing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Vrettas, Michail D.; Cornford, Dan; Opper, Manfred and Shen, Yuan (2010). A variational basis function approximation for diffusion processes. Neurocomputing, 73 (7-9), pp. 1186-1198. DOI 10.1016/j.neucom.2009.11.026

    Accepted author manuscript, 182 KB, PDF-document

Details

Original languageEnglish
Pages (from-to)1186-1198
Number of pages13
JournalNeurocomputing
Volume73
Issue7-9
DOIs
StatePublished - Mar 2010
Event17th European Symposium on Artificial Neural Networks - Bruges, Belgium

Bibliographic note

NOTICE: this is the author’s version of a work that was accepted for publication in Neurocomputing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Vrettas, Michail D.; Cornford, Dan; Opper, Manfred and Shen, Yuan (2010). A variational basis function approximation for diffusion processes. Neurocomputing, 73 (7-9), pp. 1186-1198. DOI 10.1016/j.neucom.2009.11.026

    Keywords

  • radial basis functions, dynamical systems, stochastic differential equations, parameter estimation, Bayesian inference

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