PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Approximate inference in continuous time Gaussian-Jump processes
Manfred Opper, Andreas Ruttor and Guido Sanguinetti
Advances in Neural Information Processing Systems Volume 23, pp. 1831-1839, 2010.


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 present both an exact inference algorithm based on the forward-backward recursion and 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.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Theory & Algorithms
ID Code:8038
Deposited By:Manfred Opper
Deposited On:17 March 2011