Inference for nonlinear dynamical systems, with applications to the ecology of infectious diseases.

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• Ed Ionides, University of Michigan, USA.
• Wednesday 14 November 2007, 16:00-17:00
• Center for Mathematical Sciences, MR 4.

If you have a question about this talk, please contact Dr. Leah R Johnson.

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Nonlinear stochastic dynamical models are used to study ecological systems and many other systems occuring across the sciences and engineering. Such models are natural to formulate and can be analyzed mathematically and numerically. However, difficulties associated with inference from time-series data about unknown parameters in these models have been a constraint on their application. We present a new method that makes maximum likelihood estimation feasible for partially-observed nonlinear stochastic dynamical systems (also known as state-space models) where this was not previously the case. The method is based on a sequence of filtering operations which are shown to converge to a maximum likelihood parameter estimate. We make use of recent advances in nonlinear filtering in the implementation of the algorithm. We apply the method to the study of cholera in Bangladesh. We construct confidence intervals, perform residual analysis, and apply other diagnostics. Our analysis, based upon a model capturing the intrinsic nonlinear dynamics of the system, reveals some effects overlooked by previous studies.

This talk is part of the National Centre for Statistical Ecology (NCSE) Seminars series.

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