PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Variational Inference for Markov Jump Processes
Manfred Opper and Guido Sanguinetti
In: NIPS, Vancouver, BC, Canada(2007).

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.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Theory & Algorithms
ID Code:3521
Deposited By:Guido Sanguinetti
Deposited On:11 February 2008