STAT 491: Intro to Bayesian Stats
Course Resources:
Course Schedule:
Week  Content 
Week 1: Jan 11 
Thurs. Course Overview, Quiz 1, Bayesian Thought Experiment. (PDF) (R Markdown) 
Week 2: Jan 16 Week 2: Jan 18 
Tues. Ch.2: Credibility, Models, and Parameters (PDF) (R Markdown) Thurs. HW1 due (HTML) (R Markdown), Quiz 2 Ch.4: Probability (PDF) (R Markdown) 
Week 3: Jan 23 Week 3: Jan 25 
Tues. Ch. 4: Probability Thurs. Quiz 3 Ch. 4: Probability 
Week 4: Jan 30 Week 4: Feb 1 
Tues. HW2 due (HTML) (R Markdown), Ch. 4: Probability, Part 2 (PDF) (R Markdown) Thurs. Quiz 4, Lab 1 due (HTML) (R Markdown) Ch. 4: Probability 
Week 5: Feb 6
Week 5: Feb 8 
Tues. HW3 due (HTML) (R Markdown), Lab 2 due (HTML) (R Markdown) Ch. 5: Bayes Rule (PDF) (R Markdown) Thurs. Quiz 5 Ch. 5: Bayes Rule 
Week 6: Feb 13
Week 6: Feb 15 
Tues. Quiz 6 Ch. 6: Binomial Probability (PDF) (R Markdown) (Occupancy Model Framework) Thurs. HW4 due (HTML) (R Markdown), Lab 3 due (HTML) (R Markdown) Ch. 6: Binomial Probability 
Week 7: Feb 20
Week 7: Feb 22 
Tues. Quiz 7 Ch. 7: MCMC (PDF) (R Markdown) Thurs. HW5 due (HTML) (R Markdown), Lab 4 due (HTML) (R Markdown) Ch. 7: MCMC/JAGS Project Overview (PDF) 
Week 8: Feb 27
Week 8: Mar 1 
Tues. Quiz 8 Ch. 16 Normal Distribution (PDF) (R Markdown) Thurs. Lab 5 due (HTML) (R Markdown) Ch. 16 Normal Distribution with JAGS 
Week 9: Mar 6
Week 9: Mar 8 
Tues. HW6 due (HTML) (R Markdown), Lab 6 due (HTML) (R Markdown) Exam 1 Thurs. No Class: Take Home Exam Due Saturday March 10 
Week 10: Mar 13 Week 10: Mar 15 
Tues. No Class  Spring Break Thurs. No Class  Spring Break 
Week 11: Mar 20 Week 11: Mar 22 
Tues. Project Proposal due Thurs. Ch. 9: Hierarchical Models 
Week 12: Mar 27 Week 12: Mar 29 
Tues. Ch. 9: Hierarchical Models Thurs. Ch. 17  Ch. 18: Bayesian Regression 
Week 13: April 3 Week 13: April 5 
Tues. Ch. 17  Ch. 18: Bayesian Regression Thurs. Ch. 21: Binary Regression 
Week 14: April 10 Week 14: April 12 
Tues. Ch. 21: Binary Regression Thurs. Ch. 10: Model Comparison Intermediate Project Summary 
Week 15: April 17 Week 15: April 19 
Tues. Ch. 11: Null Hypothesis Significance Testing Thurs. Ch. 12: Bayesian Testing 
Week 16: April 24 Week 15: April 26 
Tues. Exam 2 Thurs. Ch. 25.1 Reporting a Bayesian Analysis 
Finals Week: April 30 
Project Presentations and Written Summary Due 
Course Overview:
 Meeting Time: Tuesday and Thursday  9:25  10:40
 Classroom: Barnard Hall 126
 Office Hours: Tues/Thurs 11  12
Course Description
This course will introduce the basic ideas of Bayesian statistics and provide a contrast with techniques for classical inference. The course focuses on both the philosophical foundations and practical implementation of Bayesian methods.
Prerequisites
MATH 172 (Calculus II) or equivalent and STAT 217 or STAT 411. While not required experience with R will be useful.
Learning Outcomes
At the completion of this course, students will be able to:
 Describe fundamental differences between Bayesian and classical inference,
 Select priors, write likelihoods, derive posterior distributions, and verify model and prior assumptions,
 Use computer code, including R and JAGS, to sample from posterior distributions, and
 Make inferences from posterior distributions.
Textbook:
 Doing Bayesian Data Analysis, Second Edition , by John Kruschke.
Course Evaluation:

Quizzes: 10% of final grade:

There is no formal attendance policy, but there will be periodic quizzes.


Homework: 20% of final grade:
 Homework problems will be assigned every week. Students are allowed and encouraged to work with classmates on homework assignments, but each student is required to complete their own homework.

Labs: 10% of final grade:
 The course will periodically have labs, which will be organized group activities to be completed in class.

Exam 1 & Exam 2, 15% (each) of final grade
 The exams will be largely conceptual including some short mathematical derivations. The take home portions will focus on data analysis and implementation of Bayesian methods.

Project 30% of final grade
 The final project will focus on the complete data analysis cycle using relevant data: prior and model specification, posterior computation, model checking, and inference in a Bayesian context. There will be preliminary deadlines as the course progresses.