STAT 499/962 Topics in Statistics

Bayesian Inference and Decision Theory

Winter 2018 
Detailed Course Syllabus


STAT 499 / 962


Mo 11:30--12:30

Tu: 13:30--14:300

Th: 12:30--13:30


RM 116 Jeffery Hall 


Nasser Sadeghkhani





RM 518 Jeffery Hall (5th floor)

Office hours:

M 13:30-- 14:00, W 14:30--15:30

or by appointment.

Teaching Assistant:


Lecture Handouts:


Homework :

  • Assignment 01  Solution 01

  • Assignment 02  Solution 02

  • Assignment 03  Solution 03

  • Assignment 04  Solution 04


    Recommended texts:

    •  Gelman, J.B. Carlin, H.S. Stern and D.B. Rubin, Bayesian Data AnalysisSecond Edition. Chapman & Hall, 2004.
    • C. Robert, The Bayesian choice: from decision-theoretic foundations to computational implementation, Springer, 2nd ed., 2007.
    • J. Berger. Statistical Decision Theory and Bayesian Analysis, Springer Science & Business Media, 2nd ed., 1985

    Grades: It is the student's responsibility to be aware of their grades in the course and the appropriate level of work required. Your final grade in this course will depend on the following for the graduate students:

    Item for graduate students
    Portion of Grade
    15% of grade
    Midterm & Final Exam
    max (30%+ 35%, 20%+ 45%)
     of grade
    20% of grade 
For undergraduate students Homeworks 25% and project 10%.

Homework: There will about 4 homework assignments during the semester. If any assignments or project include a programming portion you can do it which any program you are more convenient with.

Exams: Midterm exam will be closed book and closed notes. Students who are unable to attend the exam for a legitimate unavoidable reason may take a make-up exam only if the student provides concrete documentation and proof of the delay and they are able to take the make-up in a very timely manner. If a make-up can't be taken the final exam/project will be reweighted for the midterm exam.


Course Description:

The course will cover Bayesian methods and decision theory with emphasis mostly on theoretical concepts. Backgrounds in the mathematical statistics and statistical computing are essential.




Course Outline:
  • Course Introduction, Some History, Bayes’ rule
  • Prior and Posterior Distributions, Improper prior distributions
  • Usual loss functions
  • Conjugate Priors
  • Minimaxity and admissibility
  • Bayesian Point Estimation
  • Confidence RegionsHierarchical and Empirical Bayes
  • Bayesian Calculations: Markov Chains, The Gibbs Sampler, Importance Sampling

 Some Useful links: