The Center for Integrated Latent Variable Research (CILVR) presents Bayesian Statistical Modeling, taught by Roy Levy in two courses.
First Course
This three-day 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 workshop 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 JAGS and R. Throughout the course participants will be able to practice exercises using these software packages; participants are encouraged to bring their own laptops to perform these exercises. (Participants will be instructed on how to download free versions of the software prior to the course.)
No prior experience with Bayesian statistical modeling is required. Participants should have a foundational knowledge of conventional (frequentist) approaches up through multiple regression. Prior experience with more advanced models and/or probability theory methods is a plus, but not required.
Second Course
This two-day course assumes experience with introductory level Bayesian statistical modeling, such as that provided in our first course (immediately preceding this course, or taken previously) or from analogous university course exposure elsewhere that covers those topics in our first course. Content covered will draw, as time allows, from such topics as: factor analysis, item response theory, structural equation modeling, latent class modeling, Bayesian networks, and multilevel modeling. Familiarity with these topics would be beneficial, but not required. Each will be reviewed from a conventional perspective before pursuing a Bayesian perspective. The presentation of each of these topics is intended to illuminate broader ideas of Bayesian statistical modeling such that key principles can be abstracted even for those researchers not working with the particular type of model at hand. Although this material is necessarily more complex, it will be presented in a manner targeting the applied researcher, with examples primarily from social science and educational research, accompanied by input and output from JAGS and R. Throughout the course participants will be able to practice exercises using these software packages; participants are encouraged to bring their own laptops to perform these exercises. (Participants will be instructed on how to download free versions of the software prior to the course.)
Participants should have foundational Bayesian knowledge, such as that provided in the first course or comparable training. Prior experience with conventional approaches to latent variable models, structural equation models, and multilevel models a plus, but not required.
Both Courses
Models and exercises for these workshops will be done using the R and JAGS software. Participants are welcome to bring their own laptops to perform these exercises, and will be instructed on how to download free versions of the software prior to the course.
Schedule
First Course: June 10-12
Second Course: June 13-14
Check-in: 8:30 AM
Continental Breakfast*: 8:30 AM - 9:00 AM
Morning Session: 9:00 AM - 12:15 PM
Lunch (on your own): 12:15 PM - 1:15 PM
Afternoon Session: 1:15 PM - 4:30 PM
* Participants who have special dietary needs or preferences are welcome to bring their own food as well.
Course Fees for the First Course: $895 for all three days; $595 for full-time students (free for registered HDQM Department faculty and students, although you must register; admission will depend on if space is available); $295 for the online option.
Course Fees for the Second Course: $595 for both days; $395 for full-time students (free for registered HDQM Department faculty and students, although you must register; admission will depend on if space is available); $195 for the online option.
Discount for taking both courses: $1295 fee for all five days; $895 for full-time students (free for pre-registered HDQM Department faculty and students, although you must register; admission will depend on if space is available); $425 for the online option.
Register today!
HDQM affiliates (all course options)
First course only (non-HDQM affiliates)
Second course only (non-HDQM affiliates)
Both courses (non-HDQM affiliates)