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:
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.
Text:
 |
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:
- 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.
Assumed background:
- 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).
Teaching Assistant:
Please contact the TA if you cannot visit during office hours to schedule an alternative time to meet.