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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 Experimental Design with Stochastic Epidemic Models

Presented by Gavin Gibson, Heriot-Watt University

17 November 2006 14:00 in CM221

Inference and parameter estimation for stochastic epidemic models has been greatly facilitated by Bayesian methods and associated computational techniques such as Markov chain Monte Carlo. The question of how experiments should be designed - e.g. how populations should be sampled in space and time - to maximise the insights gained from these analyses is now being considered. This talk will describe how the Bayesian approach to experimental design, originally due to Muller, can be applied in the context of nonlinear stochastic epidemic models. In this approach, the design itself is treated as a random quantity. A distribution, which depends fundamentally on the utility of the design, is assigned to model parameters, experimental outcome and experimental design jointly. The design which is optimal, in terms of having the highest expected utility, corresponds to the mode of the design marginal distribution. We will demonstrate how, by using approximations to parameter likelihoods based on moment closure methods, it is computationally feasible to implement this approach to design experiments in practically relevant situations. In particular, we use the methods to explore possible designs for microcosm experiments on epidemics of fungal pathogens in plant communities.

joint work with Alex Cook (School of Mathematical & Computer Sciences, Heriot-Watt University) & Chris Gilligan (Department of Plant Sciences, Cambridge University)

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