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Simulation-based maximum likelihood inference for partially observed Markov process models, by Anindya BhadraExport this event to calendar

Wednesday, January 11, 2012 — 3:00 PM EST

Anindya Bhadra, Texas A&M University

Estimation of static (or time constant) parameters in a general class of nonlinear, non-Gaussian, partially observed Markov process models is an active area of research. In recent years, simulation-based techniques have made estimation and inference feasible for these models and have offered great flexibility to the modeler. An advantageous feature of many of these techniques is that there is no requirement to evaluate the state transition density of the model, which is often high-dimensional and unavailable in closed-form. Instead, inference can proceed as long as one is able to simulate from the state transition density - often a much simpler problem. In this talk, we introduce a simlation-based maximum likelihood inference technique known as iterated filtering that uses an underlying Sequential Monte Carlo (SMC) filter. We discuss some key theoretical properties of iterated filtering. In particular, we prove the convergence of the method and establish connections between iterated filtering and well-known stochastic approximation methods. We then use the iterated filtering technique to estimate parameters in a nonlinear, non-Gaussian mechanistic model of malaria transmission and answer scientific questions regarding the effect of climate factors on malaria epidemics in Northwest India. Motivated by the challenges encountered in modeling the malaria data, we conclude by proposing an improvement technique for SMC filters used in an off-line, iterative setting.

Location 
M3 - Mathematics 3
3127
200 University Avenue West

Waterloo, ON N2L 3G1
Canada

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