Bayesian parameter estimation for latent Markov random fields and social networks
Speaker: Richard Everitt (University of Reading)
Time: 3.00 PM
Date: Thursday 8th November 2012
Location: Statistics Seminar Room L550B, 5th floor, Library Building
Abstract:
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.
Series: Statistics Seminar Series
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