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


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.

Publication DOI: https://doi.org/10.1016/j.neucom.2009.11.026
Divisions: ?? 50811700Jl ??
College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
Additional Information: 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
Uncontrolled Keywords: radial basis functions,dynamical systems,stochastic differential equations,parameter estimation,Bayesian inference,Artificial Intelligence,Computer Science Applications,Cognitive Neuroscience
Publication ISSN: 1872-8286
Last Modified: 08 May 2024 07:08
Date Deposited: 27 Apr 2012 13:26
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Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2010-03
Authors: Vrettas, Michail D.
Cornford, Dan (ORCID Profile 0000-0001-8787-6758)
Opper, Manfred
Shen, Yuan



Version: Accepted Version

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