Inference for partially observed stochastic dynamic system
Ed Ionides, Statistics, University of Michigan (February 20, 2012)
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Ed Ionides, Statistics, University of Michigan
Characteristic features of biological dynamic systems include stochasticity, nonlinearity, measurement error, unobserved variables, unknown system parameters, and even unknown system mechanisms. I will consider the resulting inferential challenges, with particular reference to pathogen/host systems (i.e., disease transmission). I will focus on statistical inference methodology which is based on simulations from a numerical model; such methodology is said to have the plug-and-play property. Plug-and-play methodology frees 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.