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Department of Mathematical Sciences

Seminar Archives

On this page you can find information about seminars in this and previous academic years, where available on the database.

Statistics Seminars: Bayesian inference for the Chemical Langevin Equation

Presented by Darren Wilkinson, Newcastle University

4 December 2007 14:15 in CM221

Biochemical network dynamics follow a continuous-time discrete-state
stochastic process governed by the Chemical Master Equation. It is of
considerable practical interest to be able to infer the rate constants
that parameterise this process using partial discrete-time
observations on the process state. Although it is possible to
construct MCMC algorithms that directly solve this problem, they do
not scale-up well to problems of interesting size and complexity. It
appears more promising to work with an approximation to the real
process, known as the Chemical Langevin Equation. Inference for this
nonlinear multivariate diffusion process is also a very challenging
problem, due to the high dependence between the process parameters and
unobserved sample paths. However, in recent years there have been a
number of interesting developments in the area of inference for
diffusions that are relevant to this problem. I will present some new
approaches to the solution of the problem that utilise MCMC,
sequential filtering, and a multivariate variant of the modified
diffusion bridge construct of Durham and Gallant. The techniques will
be illustrated in the context of some biochemical network problems.

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