A short course on likelihood-based inference for dynamic systems
Presented at University of Pennsylvania Department of Statistics, Spring 2017, by
Edward Ionides
.
Introduction
Partially observed Markov process (POMP) models: Filtering and likelihood evaluation
POMP inference via iterated filtering
Infectious disease dynamics inferred from genetic data via sequential Monte Carlo
Profile likelihood, smoothed likelihood and Monte Carlo likelihood
Panel data analysis via mechanistic models