@inproceedings{f48e51ea1686404497dd7a2dfff11b2c,
title = "Variational inference for diffusion processes",
abstract = "Diffusion processes are a family of continuous-time continuous-state stochastic processes that are in general only partially 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 multimodal. We propose a variational treatment of diffusion processes, which allows us to compute type II maximum likelihood estimates of the parameters by simple gradient 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.",
keywords = "diffusion processes, continuous-time continuous-state stochastic processes, system noise, volatility, variational free energy",
author = "C{\'e}dric Archambeau and Manfred Opper and Yuan Shen and Dan Cornford and John Shawe-Taylor",
note = "Copyright of the Massachusetts Institute of Technology Press (MIT Press); 21st Annual Conference on Neural Information Processing Systems, NIPS 2007 ; Conference date: 03-12-2007 Through 06-12-2007",
year = "2008",
language = "English",
isbn = "978-1-60560352-0",
series = "Advances In Neural Information Processing Systems",
publisher = "MIT",
pages = "17--24",
editor = "J.C. Platt and D. Koller and Y. Singer and S. Roweis",
booktitle = "Annual Conference on Neural Information Processing Systems 2007",
}