Applied Bayesian
Data Analysis

A 3-Day Remote Seminar Taught by
Roy Levy, Ph.D.

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To see a sample of the course materials, click here.

Bayesian methods have revolutionized statistics over the last quarter of a century. This is not an exaggeration. The appeal of Bayesian statistics is its intuitive basis in making direct probability statements for all assertions, and the ability to blend disparate types of data into the same model.

Bayesian models take existing knowledge and update it as new data becomes available, a principle that works across all scientific disciplines. The cost of this added inferential power is more reliance on computing. Fortunately, there are powerful software packages for Bayesian statistics that are free and easy to use (with some training).

This seminar assumes no prior experience with Bayesian statistical modeling, and is intended as both a theoretical and practical introduction. An understanding of Bayesian statistical modeling will be developed by relating it to your existing knowledge of traditional frequentist approaches. The philosophical underpinnings and departures from conventional frequentist interpretations of probability will be explained. This, in turn, will motivate the development of Bayesian statistical modeling.

Starting September 23, we are offering this seminar as a 3-day synchronous*, remote workshop. Each day will consist of a 4-hour live lecture held via the free video-conferencing software Zoom. You are encouraged to join the lecture live, but will have the opportunity to view the recorded session later in the day if you are unable to attend at the scheduled time.

Each lecture session will conclude with a hands-on exercise reviewing the content covered, to be completed on your own. An additional lab session will be held Thursday and Friday afternoons, where you can review the exercise results with the instructor and ask any questions.

*We understand that scheduling is difficult during this unpredictable time. If you prefer, you may take all or part of the course asynchronously. The video recordings will be made available within 24 hours of each session and will be accessible for four weeks after the seminar, meaning that you will get all of the class content and discussions even if you cannot participate synchronously.

Closed captioning is available for all live and recorded sessions.


To introduce Bayesian principles in familiar contexts, we will begin with simple binomial and univariate normal models, and then move to simple regression and multiple regression. Along the way, we will cover several aspects of modeling including model construction, specifying prior distributions, graphical representations of models, practical aspects of Markov chain Monte Carlo (MCMC) estimation, evaluating hypotheses and data-model fit, and model comparisons.

Although Bayesian statistical modeling has proven advantageous in many disciplines, we’ll use examples that are drawn primarily from social science and educational research. Examples will be accompanied by input and output from two freeware packages, R and Stan. There will be exercises for you to do using both of these packages.


Examples will be accompanied by input and output from the freely available Stan and R packages. Previous experience with R is desirable, but not required. You will be able to practice analyses using these packages. For those who work with Mplus, code for conducting several of the examples in Mplus will also be provided, but will not be discussed in the seminar.

If you’d like to take this course but are concerned that you don’t know enough R, there are excellent online resources for learning the basics. Here are our recommendations.

WHO SHOULD Register? 

This seminar assumes no prior experience with Bayesian statistical modeling, and is intended as both a theoretical and practical introduction. You should have a foundational knowledge of conventional frequentist approaches to statistics (e.g., hypothesis testing, confidence intervals, least-squares and likelihood estimation) in contexts up through multiple regression.

Although not required, your experience in this seminar will be enhanced by additional prior training or experience with more advanced statistical modeling techniques (e.g., general linear modeling, multivariate models for multiple outcomes) and/or by familiarity with the basics of probability theory (e.g., joint, marginal, and conditional distributions, independence).


Day 1

  • Machinery and Interpretations of Probability
  • Review of Frequentist Inference
  • Introducing Bayesian Inference
  • Bernoulli/Binomial Models
  • Summarizing Posterior Distributions
  • Accumulation of Evidence

Day 2

  • Normal Distribution Models
  • Practical Orientation to Markov chain Monte Carlo Estimation
  • Regression Models
  • Evaluating Hypotheses about Parameters & Model Comparison
  • Model Checking

Day 3

  • Incorporating Substantive Information: Regression Example (time permitting)
  • Bayesian Updating: Regression Example (time permitting)
  • Principles of Specifying Prior Distributions
  • Summary & Additional Resources

REVIEWS OF Applied Bayesian Data Analysis

“Dr. Levy is an unusually good teacher. I felt privileged to sit in his class.”
  Manuel Lombardero, University of Pittsburgh