Bayesian Multilevel Models (Berkeley, CA)

Instructor(s):

  • Ryan Bakker, University of Georgia

This workshop introduces the Bayesian multilevel model framework. Bayesian methods allow for an extremely flexible approach for estimating hierarchical models with a variety different types of dependent variables. The Bayesian approach simplifies several of the assumptions of the classical techniques for MLMS and directly estimates a variety of quantities of interest that require post-estimation methods in the non-Bayesian framework. Topics covered will be the hierarchical linear model, as well as a models with limited dependent variables, summarizing results, in and out of sample predictions, and measures of model fit. No prior knowledge of Bayesian modeling is required, but will be beneficial.

Fee: Members = $1700; Non-members = $3200

Tags: Bayes, multilevel models, hierarchical linear models

Course Sections

Section 1

Location: University of California, Berkeley -- Berkeley, CA

Date(s): July 31 - August 4

Time: 9:00 AM - 5:00 PM

Instructor(s):

  • Ryan Bakker, University of Georgia

Syllabus: