STAT 576 Bayesian Analysis

Fall 2024


Course Info

Syllabus (PDF)

Lecture Notes

  1. Lecture 0: Overview [Lecture Version] [Handout Version]
  2. Lecture 1: Review on Prerequisites [Lecture Version] [Handout Version]
  3. Lecture 2: Bayesian Inference I [Lecture Version] [Handout Version]
  4. Lecture 3: Bayesian Inference II [Lecture Version] [Handout Version]
  5. Lecture 4: Asymptotic Properties of Bayesian Inference [Lecture Version] [Handout Version]
  6. Lecture 5: Hierarchical Models [Lecture Version] [Handout Version]
  7. Lecture 6: Model Checking [Lecture Version] [Handout Version]
  8. Lecture 7: Bayesian Computation [Lecture Version] [Handout Version]
  9. Lecture 8: Markov Chain Simulation [Lecture Version] [Handout Version]
  10. Lecture 9: Hybrid Markov Chain [Lecture Version] [Handout Version]
  11. Lecture 10: State Space Models and Sequential Monte Carlo I [Lecture Version] [Handout Version]
  12. Lecture 11: State Space Models and Sequential Monte Carlo II [Lecture Version] [Handout Version]
  13. Lecture 12: Bayesian Regression Models [Lecture Version] [Handout Version]
  14. Lecture 13: Nonparametric Models [Lecture Version] [Handout Version]

Homework

  1. Homework 1. Due Sept. 18. [Problems] [Solution]
  2. Homework 2. Due Oct. 4. [Problems] [Solution]
  3. Homework 3. Due Nov. 1. [Problems]
  4. Homework 4. Due Dec. 13. [Problems]