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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: MCMC algorithms for parameters governing multivariate SDEs with application to systems biology models

Presented by Andrew Golightly, School of Mathematics & Statistics, Newcastle University

11 June 2010 14:15 in CM221

Methods for inferring rate constants of stochastic kinetic models associated with Biochemical
networks are now reasonably well developed. Whilst it is possible to work with the exact
discrete stochastic model for inference, computational cost can be prohibitive for networks
of realistic size and complexity. By treating the numbers of molecules of biochemical species
as continuous, a diffusion approximation can be used so that rate constants correspond to
the parameters entering into the drift and diffusion coefficients of a nonlinear SDE. Unfortunately,
Bayesian inference is problematic since closed form transition densities are rarely tractable.
One widely used solution involves the introduction of latent data points between every pair
of observations to allow a sufficiently accurate Euler-Maruyama approximation of the
transition densities. Markov chain Monte Carlo (MCMC) methods can then be used to sample
the posterior distribution of latent data and model parameters; however, naive schemes
suffer from a mixing problem that worsens with the degree of augmentation. We will consider
some recently developed MCMC (and particle MCMC) schemes that are not adversely affected by
the amount of augmentation.

Host: Umberto Picchini

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