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.