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 

 TuesProject 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:

  1. Describe fundamental differences between Bayesian and classical inference,
  2. Select priors, write likelihoods, derive posterior distributions, and verify model and prior assumptions,
  3. Use computer code, including R and JAGS, to sample from posterior distributions, and 
  4. Make inferences from posterior distributions.

Textbook:

  1. 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.