Approximate inference in continuous time Gaussian-Jump processes

Manfred Opper, Andreas Ruttor, Guido Sanguinetti

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.
Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 23
EditorsJ.D. Lafferty, C.K.I. Williams, J. Shawe-Taylor, R.S. Zemel, A. Culotta
Number of pages9
Publication statusPublished - 2010


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