PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Approximate inference in continuous time Gaussian-Jump processes
Manfred Opper, Andreas Ruttor and Guido Sanguinetti
In: NIPS 2010, Dec 2010, Vancouver.

Abstract

We present a novel approach to inference in conditionally Gaussian continuous time stochastic processes, where the latent process is a Markovian jump process. We first consider the case of jump-diffusion processes, where the drift of a linear stochastic differential equation can jump at arbitrary time points. We derive partial differential equations for exact inference and present a very efficient mean field approximation. By introducing a novel lower bound on the free energy, we then generalise our approach to Gaussian processes with arbitrary covariance, such as the non-Markovian RBF covariance. We present results on both simulated and real data, showing that the approach is very accurate in capturing latent dynamics and can be useful in a number of real data modelling tasks.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
ID Code:7626
Deposited By:Guido Sanguinetti
Deposited On:17 March 2011