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Monday, April 08, 2019
1:30 PM - 2:30 PM
CNLS Conference Room (TA-3, Bldg 1690)

Seminar

Likelihood-based inference for complex partially observed systems, with applications to genetic sequences, panel data and spatiotemporal data

Edward Ionides
University of Michigan

Sequential Monte Carlo algorithms enable computation of the likelihood function for general partially observed Markov process (POMP) models. A POMP model consists of a latent Markov process observed via a collection of noisy measurements. A collection of independent POMP models with some shared parameters is called a PanelPOMP model. A POMP model for which the Markov process has a tree-valued structure appropriate for disease transmission modeling, with measurements of both tree-related quantities (i.e., genetic sequences) and population quantities, is called a GenPOMP. A high-dimensional POMP model comprised of many coupled units is called a SpatPOMP since each unit may correspond to a spatial location. We discuss advances in the theory and practice of inference for POMP, PanelPOMP, GenPOMP and SpatPOMP models via sequential Monte Carlo. From a data analysis perspective, we demonstrate software for working with these model classes. From a methodological perspective, we discuss the principles underlying our algorithms.

Host: Ethan Obie Romero-Severson