Advanced Stochastic Modeling
Course material for STA 531
Instructor: Jeff Miller
Spring 2016, Duke University
Department of Statistical Science
Synopsis
Building probabilistic models and performing inference with them. Discrete and continuous models, univariate and multivariate models, hierarchical models, graphical models, time-series models, mixture models. Model checking and robustness. Advanced computational methods for efficient inference. Familiarity with probability calculus, linear algebra, computer programming, and the basics of Bayesian statistics will be assumed.
General information
This course was primarily based on my lecture notes (Lecture Notes on Advanced Stochastic Modeling, Jeffrey W. Miller, 2016).
Textbooks:
Lecture notes
Homework assignments
Exams