PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Approximate inference for stochastic reaction processes.
Andreas Ruttor, Guido Sanguinetti and Manfred Opper
In: Learning and Inference in Computational Systems Biology (2009) MIT Press .

Abstract

We discuss the problem of statistical inference for Markov jump processes modelling biochemical reactions. Using a variational formulation of exact inference we derive two different approximations. A weak noise approach within a diffusion approximation is relevant when the number of individuals of a given species is rather large. On the other hand a mean field approximation takes the discreteness of the number of individuals into account but neglects correlations.

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EPrint Type:Book Section
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
ID Code:5262
Deposited By:Manfred Opper
Deposited On:24 March 2009