Bayesian Data Analysis (Stat 568)
Spring 2020

General Information

Lecturer Zhiqiang Tan
Office: 459 Hill Center
Email: ztan at stat.rutgers.edu

Lectures: Th 6:40-9:30PM, HIL 552
Office hours: MTh 1:30-2:30PM.
Prerequisite Stat 583 or equivalent.
Textbook Gelman et al (2014) Bayesian Data Analysis (3rd edition), CRC Press.
Topics
Parametric models Chapters 2-3
Hierarchical models Chapter 5
Model checking Chapters 6-7
Bayesian computation Chapters 10-13
Regression models Chapters 14-16
Mixture models Chapter 22
Exams and Homework There will be 1 midterm project and a final project.
Homework will be assigned and collected.
Grading The final grade will be based on the following components with the weights:
Homework: 60%
Final Project: 40%
Makeup policy Make-up exams will only be given if written documentation of a major outside circumstance is provided by a dean or a doctor.
Students who miss exams without presenting proper documentation in a timely manner will receive a grade of zero.
Homework Assignments HW 1, HW 2, HW 3, HW 4.

Solution to HW 1.
Solution to HW 2.
Please see Canvas Files for solutions to HW 3.
Announcements Feb 27:
        R codes for examples on bioassay data (here) and eight schools (here).
March 26:
        R codes for studying a discrete Markov chain (here).
        R codes for Metropolis sampling (here) and Gibbs sampling (here) from bivariate normal distributions.
April 2:
        Expanded R codes for Metropolis sampling (here) and Gibbs sampling (here) from bivariate normal distributions.
April 9:
        R codes for Gibbs sampling (here) for posterior simulation in the eight-school example.
        R codes for Gibbs sampling (here) for posterior simulation in the coagulation example.
April 16:
        R codes for logit regression (here). The dataset is here.
April 30:
        R codes for Metropolis sampling (here) for posterior simulation in the coagulation example.
        R codes for probit regression (here).