PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Variational Inference for Diffusion Processes
Cedric Archambeau, Manfred Opper, Yuan Shen, Dan Cornford and John Shawe-Taylor
Advances in Neural Information Processing Systems 2007 2007.

Abstract

Diffusion processes are a family of continuous-time continuous-state stochastic processes that are in general only partly observed. The joint estimation of the forcing parameters and the system noise (volatility) in these dynamical systems is a crucial, but non-trivial task, especially when the system is nonlinear and multi-modal. We propose a variational treatment of diffusion processes, which allows us to compute type II maximum likelihood estimates of the parameters by simple gradient descent techniques and which is computationally less demanding than most MCMC approaches. We also show how a cheap estimate of the posterior over the parameters can be constructed based on the variational free energy.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:3706
Deposited By:Manfred Opper
Deposited On:14 February 2008