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Approximate inference in continuous time Gaussian-Jump processes
Manfred Opper · Andreas Ruttor · Guido Sanguinetti

Wed Dec 08 12:00 AM -- 12:00 AM (PST) @ None #None

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.

Author Information

Manfred Opper (Technische Universitaet Berlin)
Andreas Ruttor (TU Berlin)
Guido Sanguinetti (University of Edinburgh)

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