PSY 646: Bayesian Statistics for Psychological Science
Days/times: Tuesday, Thursday / 9:00 am - 10:15 am
Location: PRCE 255 (will have computers with appropriate software installed)
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):
- PPT slides for Lecture 1.
- PPT slides for Lecture 2.
- PPT slides for Lecture 3.
- PPT slides for Lecture 4.
- PPT slides for Lecture 5, Shrinkage.R, ShrinkagePrediction.R.
- PPT slides for Lecture 6, VisualSearch.csv, VisualSearch1.R.
- PPT slides for Lecture 7, VisualSearch2.R.
- PPT slides for Lecture 8, SmilesLeniency.csv, SmilesLeniency1.R, SmilesLeniency2.R.
- PPT slides for Lecture 9, PhysiciansWeight.csv, PhysiciansWeight1.R.
- Homework 1, ProspectiveMemoryEyeTrackingHW1.csv.
- PPT slides for Lecture 10, WeaponPrime.csv, WeaponPrime1.R, ADHDTreatment.csv, ADHDTreatment1.R, ADHDTreatment2.R.
- PPT slides for Lecture 11, VisualSearch3.R.
- PPT slides for Lecture 12, SmilesLeniency4.R, VisualSearch4.R.
- Homework 2, SleepySubjects.csv.
- PPT slides for Lecture 13, SmilesLeniency4.R, ADHDTreatment4.R.
- If you have struggled get brms/STAN installed on your computer, you might try a just released web version. Details are in a post at Andrew Gelman's blog. Update: seems to be most for demonstrative purposes. Cannot handle large data sets.
- PPT slides for Lecture 14, ZennerCards1.R, ZennerCards.csv, Driving1.R, Driving.csv.
- PPT slides for Lecture 15.
- Homework 3, LookDontType.csv.
- Presentation Instructions.
- PPT slides for Lecture 16.
- PPT slides for Lecture 17, FacialFeedback1.R, FacialFeedback2.R, FacialFeedback.csv.
- PPT slides for Lecture 18.
In case you do not yet have the textbook, Chapter 1 of the textbook is on-line.
McElreath, R Statistical Rethinking: A Bayesian Course with Examples in R and Stan. Ordering information and code examples are at the book web site.
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:
- Explain some problems/difficulties with frequentist methods: Publication bias, optional stopping, questionable research practices.
- Discuss differences between hypothesis testing and prediction: mean squared error, shrinkage.
- Discuss methods for prediction: likelihood, AIC, BIC, cross-validation, lasso.
- Explain the basic ideas of Bayesian methods: non-informative priors, informative priors.
- Provide hands-on examples of applying Bayesian methods: Bayes Factors, hierarchical models.
- 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 presentation 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.
- It would be nice, but not necessary, if you had some previous exposure to calculus.
- Doing any kind of Bayesian analysis requires some programming. We will be using the free R studio program. You do not need to be an expert programmer, but if you have little programming experience, you will have some catching up to do.
- Students should have experience with typical statistical methods (t-test, ANOVA, regression).
Please contact the TA if you cannot visit during office hours to schedule an alternative time to meet.