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Poster

Moment-Based Variational Inference for Markov Jump Processes

Christian Wildner · Heinz Koeppl

Pacific Ballroom #212

Keywords: [ Approximate Inference ] [ Computational Biology and Genomics ] [ Time Series and Sequence Models ]


Abstract:

We propose moment-based variational inference as a flexible framework for approximate smoothing of latent Markov jump processes. The main ingredient of our approach is to partition the set of all transitions of the latent process into classes. This allows to express the Kullback-Leibler divergence from the approximate to the posterior process in terms of a set of moment functions that arise naturally from the chosen partition. To illustrate possible choices of the partition, we consider special classes of jump processes that frequently occur in applications. We then extend the results to latent parameter inference and demonstrate the method on several examples.

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