Bayesian Statistical Modeling: A First Course - Day 3

Online

The Center for Integrated Latent Variable Research (CILVR)

presents

BAYESIAN STATISTICAL MODELING: A FIRST COURSE, JUNE 7-9, 2021

and

BAYESIAN STATISTICAL MODELING: A SECOND COURSE, JUNE 10-11, 2021

taught by

Roy Levy, Arizona State University


Short Description

Bayesian Statistical Modeling: A First Course

This online, three-day short course 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 participants’ 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. It is assumed that participants have expertise with frequentist approaches to statistics (e.g., hypothesis testing, confidence intervals, least-squares and likelihood estimation) in contexts up through multiple regression. Although not required, a participant’s experience in this course will be enhanced by additional prior coursework or experience with 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).

To introduce Bayesian principles in familiar contexts we will begin with simple binomial and univariate normal models, then move to simple regression and multiple regression. Along the way, we will cover aspects of modeling including model construction, graphical representations of models, practical aspects of Markov chain Monte Carlo (MCMC) estimation, evaluating hypotheses and data-model fit, model comparisons, and modeling in the presence of missing data. Although Bayesian statistical modeling has proven advantageous in many disciplines, the examples used in presentations draw primarily from social science and educational research. Examples will be accompanied by input and output from Stan and R. Throughout the course participants will be able to practice exercises at home using these software packages. (Participants will be instructed on how to download free versions of the software prior to the course.)

Course Fees

First Course Only:
      -     Professional: $495
      -     Full-time student*: $295
Second Course Only:
      -     Professional: $345
      -     Full-time student*: $195
Both First and Second Course:
      -     Professional: $795
      -     Full-time student*: $455

*Full-time students must submit student status proof at https://go.umd.edu/CILVR-STUDENT for prompt processing of the registration.

Free for registered HDQM Department faculty and HDQM degree-seeking students, although you must register through the internal link. 

REFUND POLICY: Full refund if cancellation occurs at least 10 business days prior to the workshop date; 50% refund if within 10 days of the first day of the course.

 

More details and registration instructions available on the BAYES-2021 Workshop page.

For any questions, please contact Yishan Ding at bayes.cilvr@gmail.com.