Class Lectures: Tue, Thu 4:10 PM - 5:25 PM, HE 920 Professor Saad Mneimneh, HN 1090F Office hours: by appointment

Textbooks
Bayesian statistics: an introduction, Peter Lee
Bayesian computation with R, Jim Albert

In addition to some notes that will be provided from time to time

Lectures
Lecture 1 - Probability axioms, independence, conditioning
Lecture 2 - Multiplication rule, Bayes' rule, examples of Bayesian approach
Lecture 3 - Discrete random variable, PMF, expectation, variance, example PMFs: binomial, geometric, uniform
Lecture 4 - Continuous random variable, from PMF to PDF, example with conditional uniform PDFs
Lecture 5 - Binomial and Poisson approximation, exponential distribution
Lecture 6 - DeMoivre-Laplace approximation, Gaussian (normal) distribution
Lecture 8 - Lab, computing binomial probabilities/posteriors with R
Lecture 9 - Conjugate forms, Gaussian prior
Lectures 10, 11 - Uniform prior, Lindley's paradox, Gamma function
Lecture 12, 13 - Chi-squared distribution as a fitness test and a conjugate prior
Lecture 14-15 Student distribution, dealing with unknown mean and variance
Lecture 16 - Beta prior, Polya's urn
Lecture 16 - Markov chains
Lecture 17 - Hidden Markov models and Vierbi algorithm
Lecture 18 - Dynamic programming, Viterbi

Notes
Note 1
Note 2
Note 3
Note 4
Note 5
Note 6
Note 7
Note 8
Note 9

Homework
Homework 1 Due 09/15/09 Solution
Homework 2 Due 10/01/09 Solution
Homework 3 Due 10/22/09 Solution
Homework 4 Due 11/12/09

Grading
Homework 20% (or 30% if no project)
QUIZ 1 20%
QUIZ 2 20%
Final 30%
Project (optional for STAT 319 students) 10%