Bayesian parameter estimation for latent Markov random fields and social networks
Richard Everitt (University of Reading)
Friday 22nd February, 2013 15:00-16:00 Maths 203
In a range of applications, including population genetics, epidemic modelling and social network analysis, the data from which we wish to estimate parameters of interest consists of noisy or incomplete observations of an unobserved process. Bayesian statistics offers a framework in which to tackle this problem, accurately accounting for the uncertainty present due to the missing data. However, standard Markov chain Monte Carlo (MCMC) methods that are used to implement the Bayesian approach can perform poorly in this situation. In this talk we describe two alternatives to standard MCMC approaches: approximate Bayesian computation (ABC) and particle MCMC. Both methods are applied to parameter estimation of a hidden Markov random field, and are compared to the standard data augmentation approach.