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.
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
- radial basis functions
- dynamical systems
- stochastic differential equations
- parameter estimation
- Bayesian inference