STA 293: SPECIAL TOPICS
Professors Athanasios Kottas
& Robert Wolpert, Instructors
TTh 10:55 - 12:10pm
025 Old Chemistry Building
Topic 1: Bayesian Nonparametric Modeling (Kottas)
Handouts
1. Bayesian nonparametrics
2. The Dirichlet process
3. Dirichlet process mixture models
4. References
Description: Brief review of priors on spaces of random functions.
Dirichlet process and Dirichlet process mixtures with emphasis on
methodological applications, modeling approaches and implementation using
Markov chain Monte Carlo methods.
Grading/prerequisites: Evaluations will be based on in class
student presentations on topics from the recent literature. Knowledge of
probability theory (preferably at the level of STA 205) and basic
techniques of Bayesian computing will be assumed.
Topic 2: Inference for Stochastic
Processes (Wolpert)
Description: An introduction to statistical inference from
observations of stochastic processes. Examples include inference about
Markov chains, Gaussian processes, diffusion processes, and Levy
processes.
Schedule: An on-line syllabus is available.
Grading/prerequisites: Evaluations will be based on (group or
individual) brief student presentations of topics chosen from the
literature in collaboration with the instructor. No examinations.
Students are expected to be familiar with probability theory at the level
of STA205 or MTH216 or MTH287.