Variational inference for Markov jump processes

Manfred Opper, Guido Sanguinetti

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

Abstract

Markov jump processes play an important role in a large number of application domains. However, realistic systems are analytically intractable and they have traditionally been analysed using simulation based techniques, which do not provide a framework for statistical inference. We propose a mean field approximation to perform posterior inference and parameter estimation. The approximation allows a practical solution to the inference problem, {while still retaining a good degree of accuracy.} We illustrate our approach on two biologically motivated systems.
Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 20 (NIPS 2007)
EditorsJ.C. Platt, D. Koller, Y. Singer, S.T. Roweis
Pages1105-1112
Number of pages8
Publication statusPublished - 2008

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