Course:

STAT 499 / 962

Time:

Mo 11:3012:30
Tu: 13:3014:300
Th: 12:3013:30

Place:

RM 116 Jeffery Hall

Instructor:

Nasser Sadeghkhani

Email:

a.sadeghkhani@queensu.ca

Telephone

6135332427

Office:

RM 518 Jeffery Hall (5th floor)

Office hours:

M 13:30 14:00, W 14:3015:30
or by appointment.

Teaching Assistant:

NA

Lecture Handouts:
Homework :




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 decisiontheoretic 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

Homework

15% of grade

Midterm & Final Exam

max (30%+ 35%, 20%+ 45%)
of grade

project

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 makeup exam only if the student provides concrete documentation and proof of the delay and they are able to take the makeup in a very timely manner. If a makeup 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:
EXAM PREPARATION:
