Applied Bayesian Data Analysis, September 2022

Event Phone: 1-610-715-0115

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

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.

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.

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