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STAT 882: Nonparametric Bayesian Inference

Winter Quarter 2009

^Instructors:| |Steve MacEachern. | |office Hours: Friday 9:30 - 10:30am in 205C Cockins Hall, and by appointment|

Xinyi Xu. office Hours: Tuesday 9:30 - 10:30am in 440G Cockins Hall, and by appointment

^Lecture Hours & Location:| |TTh 1:30-2:48pm, Baker Systems Engineering (BE) 134A |

Text:  There is no required text book for this course. The lectures will be based on the instructors' notes and a collection of papers that will be handed out during the quarter.

Course syllabus


  • Homework 1 (both parts) will be due on Thursday, Jan. 22.



  1. Week 1: Introduction to nonparametric Bayesian methods. Motivating examples. Consistency, false consistency, and principle driven modelling. References:
    • Berger, J.O. (1982). Statistical Decision Theory and Bayesian Analysis, 2nd edition. Springer-Verlag: New York.
    • Savage, L.J. (1954). The Foundations of Statistics. Wiley: New York.
  2. Week 2: From parametric Bayesian inference to nonparametric Bayesian inference. The constructions and properties of Dirichlet process. References:
  3. Week 3: Simple applications of Dirichlet process. Polya urn schemes. Sethuraman's representation. Posterior consistency. References:
  4. Week 4: Dirichlet process mixtures. Computational methods. References:
  5. Week 5: Computational methods for mixtures of Dirichlet process. References:
  6. Example codes:
  7. Week 6: More on computational issues. Applications of Dirichlet process priors in Density estimation. References:
  8. Example codes:
  9. Week 7: Applications of Dirichlet process priors in clustering/classification and regression problems. References:
  10. Week 8: Applications of Dirichlet process priors in regressions (Cont.) An example of NP regression v.s. parametric regression:
  11. References:


Last Update: Feburary 26, 2009.

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