Statistics Seminar: STA 290
Wednesday, May 9th, 2012 at 10:00am, MSB 1147 (Colloquium Room)
Refreshments prior to seminar in MSB 4110 (Statistics Lounge)
Speaker: Edward Ionides (University of Michigan)
Title: Inference for partially observed stochastic dynamic systems
Abstract: Inferential challenges arise in the study of biological dynamic systems due to the combination of stochasticity, nonlinearity, measurement error, unobserved variables, unknown system parameters, and unknown system mechanisms. I will discuss statistical methodology developed to address these challenges, with particular reference to pathogen/host systems (i.e., disease transmission). I will focus on methodology which is based on simulations from a numerical model; such methodology is said to have the plug-and-play property. Plug-and-play approaches free the modeler from an obligation to work with models for which transition probabilities are analytically tractable. A recent advance in plug-and-play likelihood-based inference for general partially observed Markov process models has been provided by the iterated filtering algorithm. I will discuss the theory and practice of iterated filtering.