Variational inference for diffusion processes

Archambeau, Cédric, Opper, Manfred, Shen, Yuan, Cornford, Dan and Shawe-Taylor, John (2008). Variational inference for diffusion processes. IN: Annual Conference on Neural Information Processing Systems 2007. Platt, J.C.; Koller, D.; Singer, Y. and Roweis, S. (eds) Advances In Neural Information Processing Systems . CAN: MIT.

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.

Divisions: Engineering & Applied Sciences > Computer Science
Engineering & Applied Sciences > Systems analytics research institute (SARI)
Additional Information: Copyright of the Massachusetts Institute of Technology Press (MIT Press)
Event Title: 21st Annual Conference on Neural Information Processing Systems, NIPS 2007
Event Type: Other
Event Dates: 2007-12-03 - 2007-12-06
Uncontrolled Keywords: diffusion processes,continuous-time continuous-state stochastic processes,system noise,volatility,variational free energy
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Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
Published Date: 2008
Authors: Archambeau, Cédric
Opper, Manfred
Shen, Yuan
Cornford, Dan ( 0000-0001-8787-6758)
Shawe-Taylor, John

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Version: Accepted Version


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