Seminar: Bayesian inference for Markov processes with application to biochemical network dynamics

SpeakerDarren Wilkinson
AffiliationNewcastle University
DateThursday, 15 Mar 2012
Time13:00 - 14:00
LocationRoom 102, 1-19 Torrington Place
Event seriesCSML Joint Seminar Series

A number of interesting statistical applications require the
estimation of parameters underlying a nonlinear multivariate
continuous time Markov process model, using partial and noisy discrete
time observations of the system state. Bayesian inference for this
problem is difficult due to the fact that the discrete time transition
density of the Markov process is typically intractable and
computationally intensive to approximate. It turns out to be possible
to develop MCMC algorithms which are exact, provided that one can
simulate exact realisations of the process forwards in time. Such
algorithms, often termed "likelihood free" or "plug-and-play" are very
attractive, as they allow separation of the problem of model
development and simulation implementation from the development of
inferential algorithms. Such techniques break down in the case of
perfect observation or high-dimensional data, but more efficient
algorithms can be developed if one is prepared to deviate from the
likelihood free paradigm, at least in the case of diffusion processes.
The methods will be illustrated using examples from population
dynamics and stochastic biochemical network dynamics.

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