A variational radial basis function approximation for diffusion processes

Michail D. Vrettas; Dan Cornford; Yuan Shen

A variational radial basis function approximation for diffusion processes

Michail D. Vrettas
, Dan Cornford
, Yuan Shen

Computer Science Research Group

Aston University

Research output: Chapter in Book/Published conference output › Conference publication

2 Citations (Scopus)

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 language	English
Title of host publication	ESANN 2009 proceedings, 17th European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning
Pages	497-502
Number of pages	6
Publication status	Published - 2009
Event	17th European Symposium on Artificial Neural Networks: Advances in Computational Intelligence and Learning - Bruges, Belgium Duration: 22 Apr 2009 → 24 Apr 2009

Conference

Conference	17th European Symposium on Artificial Neural Networks
Abbreviated title	ESANN 2009
Country/Territory	Belgium
City	Bruges
Period	22/04/09 → 24/04/09

Cite this

@inproceedings{58ec1eb4466f441e8d54dc22ef98c479,

title = "A variational radial basis function approximation for diffusion processes",

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.",

author = "Vrettas, \{Michail D.\} and Dan Cornford and Yuan Shen",

year = "2009",

language = "English",

isbn = "2-930307-09-9",

pages = "497--502",

booktitle = "ESANN 2009 proceedings, 17th European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning",

note = "17th European Symposium on Artificial Neural Networks : Advances in Computational Intelligence and Learning, ESANN 2009 ; Conference date: 22-04-2009 Through 24-04-2009",

}

TY - GEN

T1 - A variational radial basis function approximation for diffusion processes

AU - Vrettas, Michail D.

AU - Cornford, Dan

AU - Shen, Yuan

PY - 2009

Y1 - 2009

N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/79957514371

M3 - Conference publication

AN - SCOPUS:79957514371

SN - 2-930307-09-9

SN - 978-2-930307-09-1

SP - 497

EP - 502

BT - ESANN 2009 proceedings, 17th European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning

T2 - 17th European Symposium on Artificial Neural Networks

Y2 - 22 April 2009 through 24 April 2009

ER -

A variational radial basis function approximation for diffusion processes

Abstract

Conference

Other files and links

Fingerprint

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

Cite this