Module dates/times: Wednesday, July 17, 1:30-5 p.m.; Thursday, July 18, 8:30 a.m.-5 p.m., and Friday, July 19, 8:30 a.m.-5 p.m.

Prerequisites: Students are expected to have a working knowledge of the R computing environment. Programming will be in R. Students new to R should complete a tutorial before the module. This module assumes knowledge of the material in Module 1: Probability and Statistical Inference, though not necessarily from taking that module.

This module introduces statistical inference techniques and computational methods for dynamic models of epidemiological systems. The course will explore deterministic and stochastic formulations of epidemiological dynamics and develop inference methods appropriate for a range of models. Special emphasis will be on exact and approximate likelihood as the key elements in parameter estimation, hypothesis testing, and model selection. Specifically, the course will cover sequential Monte Carlo, iterated filtering, and model criticism techniques. Students will learn to implement these in R to carry out maximum likelihood and Bayesian inference.

Prerequisites: Students are expected to have a working knowledge of the R computing environment. Programming will be in R. Students new to R should complete a tutorial before the module. This module assumes knowledge of the material in Module 1: Probability and Statistical Inference, though not necessarily from taking that module.

This module introduces statistical inference techniques and computational methods for dynamic models of epidemiological systems. The course will explore deterministic and stochastic formulations of epidemiological dynamics and develop inference methods appropriate for a range of models. Special emphasis will be on exact and approximate likelihood as the key elements in parameter estimation, hypothesis testing, and model selection. Specifically, the course will cover sequential Monte Carlo, iterated filtering, and model criticism techniques. Students will learn to implement these in R to carry out maximum likelihood and Bayesian inference.