STAT 535 (Introduction to Bayesian Data Analysis)

Spring 2024

Instructor

David Hitchcock, associate professor of statistics

Syllabus

Syllabus: (pdf document)

Office Hours -- Spring 2024

Mon-Wed-Fri 1:00-2:00 pm, Tuesday 1:30-2:30 pm, or please feel free to make an appointment to see me at other times.

Office: 219B LeConte College
Phone: 777-5346
E-mail: hitchcock@stat.sc.edu

Class Meeting Time

MWF 12:00 - 12:50 pm, LeConte College 103

Prerequisites:

STAT/MATH 511 and STAT 515 or equivalent, or STAT 582(=CSCE 582).

Current Textbook:

Bayes Rules! An Introduction to Applied Bayesian Modeling, by Alicia A. Johnson, Miles Q. Ott, Mine Dogucu. CRC Press, 2022.
Online version available at: https://www.bayesrulesbook.com
Other Resource (not required): Kruschke, John K. Doing Bayesian Data Analysis, Second Edition. Academic Press, 2015.

Course Outline:

Topics covered include: Principles of Bayesian statistics; one- and two-sample Bayesian models; Bayesian linear and generalized linear models; Monte Carlo approaches to model fitting; Prior elicitation; Hypothesis testing and model selection; Complex error structures, hierarchical models; Statistical packages such as BUGS/WinBugs, R, or SAS.

Learning Objectives: By the end of the term successful students should be able to do the following:

  • Understand the philosophy of Bayesian statistical modeling
  • Understand Bayesian models for numerous common data analysis situations, including prior elicitation
  • Use software such as R, BUGS, or SAS to implement Bayesian analyses
  • Understand basic principles of both conjugate analyses and MCMC-based Bayesian analyses

    Graded Assignments

    Two exams, plus a final exam. Occasional homework assignments.

    Lecture Slides from Class:

    Computing Tips and Examples: R

    R Examples from Previous Book (not needed for Spring 2024)

    Homework

    NOTE: When you turn in the homeworks, you may either turn in a hard copy to me by hand, or you may submit the homework to me through uploading into Blackboard (Word document or pdf is preferred for submissions via Blackboard).

    (Instructions for uploading via Blackboard) (NOTE: Please use Chrome or Firefox when uploading assignments in Blackboard, not Safari!)

    Homework Solutions

    Data Sets

    Information about Final Exam

    Information about Project

    Preliminary Information about the Project for Spring 2024: (pdf document)

    The project will be due April 18 by 10:00 p.m. (This applies to the written report and the short written summary or video presentation of your project)

    Further Information about the Project for Spring 2024: (pdf document) or (Word document)

    Review Sheets for Exams

    Practice Problems

    Formula Sheets for Exams

    PLEASE BRING A PENCIL AND CALCULATOR TO THE EXAM!

    Exam Solutions

    Exams