PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Variational inference for Markov jump processes
Manfred Opper and Guido Sanguinetti
Advances in Neural Information Processing Systems 2007 2008.

Abstract

Discrete stochastic 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 some important features of the original problem such as the existence of emerging properties. We illustrated our approach on two biologically motivated systems.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:3707
Deposited By:Manfred Opper
Deposited On:14 February 2008