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PSY 646: Bayesian Statistics for Psychological Science


Fall 2018
Days/times: Tuesday, Thursday / 9:00 am - 10:15 am
Location: PRCE 255 (will have computers with appropriate software installed)

Instructor:

NameOffice EmailPhoneOffice hours
Greg FrancisPSYCH 3186gfrancis@purdue.edu494-6934 MWF 2:00 - 3:00 pm
Please contact me (email is best) if you cannot visit during office hours to schedule an alternative time to meet.

Materials (lectures, readings, datasets, code):

Text:
Rethinking Statistics web site McElreath, R Statistical Rethinking: A Bayesian Course with Examples in R and Stan. Ordering information and code examples are at the book web site.
In case you do not yet have the textbook, Chapter 1 of the textbook is on-line.

General plan: The course will explain why you might want to use Bayesian methods instead of frequentist methods (such as t-tests, ANOVA, or regression). The general plan is to:

  1. Explain some problems/difficulties with frequentist methods: Publication bias, optional stopping, questionable research practices.
  2. Discuss differences between hypothesis testing and prediction: mean squared error, shrinkage.
  3. Discuss methods for prediction: likelihood, AIC, BIC, cross-validation, lasso.
  4. Explain the basic ideas of Bayesian methods: non-informative priors, informative priors.
  5. Provide hands-on examples of applying Bayesian methods: Bayes Factors, hierarchical models.
  6. Discuss ways to make decisions: utility.

Throughout, we will be using computer programs to demonstrate the ideas. There will not be any proofs.

Class home page: The home page for this course is http://www.psych.purdue.edu/~gfrancis/Classes/PSY646/indexF18.html From this page you can download lecture notes, view the class schedule, view current grades, and connect to the various homework laboratory assignments.

Homework: Assignments will be due approximately every two weeks. The intention is to use the homework assignments as a way of practicing the concepts we discuss in class. They will be graded, but only to insure that students actively participate.

Project: In the last two weeks, students will present a Bayesian (or related) analysis of some of their own data. If you do not happen to have a data set, we will get one for you.

Assumed background: