Thursday, 7 June 2012

Introduction to Bayesian Hierarchical and Multi-level Models

Much research involves sampling from groups or subpopulations at different levels - classes, schools, etc.  Peter Congdon's online course "Introduction to Bayesian Hierarchical and Multi-level Models," teaches you how to incorporate prior information about these groups into Bayesian models. For more details please visit at

"Introduction to Bayesian Hierarchical and Multi-level Models" extends the Bayesian modeling framework to cover hierarchical models and to add flexibility to standard Bayesian modeling problems.  Participants will learn how to define three stage hierarchical models and to implement them using Winbugs, in multilevel, meta-analytic and regression applications.  Continuous, count and binary outcomes are covered.  Participants will also learn how to assess goodness-of-fit.

Dr. Peter Congdon is a Research Professor in Quantitative Geography and Health Statistics at Queen Mary University of London.  He is the author of several books and numerous articles in peer-reviewed journals.  His research interests include spatial data analysis, Bayesian statistics, latent variable models and epidemiology.

Who can take this course:
Statistical analysts with some familiarity with Bayesian analysis who want to deepen their skill set in Bayesian modeling.

Course Program:

Course outline: The course is structured as follows
SESSION 1 - Defining Bayesian Hierarchical Models
  • Overview of application contexts: meta-analysis to summarise accumulated evidence; comparisons of related units (e.g. "league table comparisons" of exam results, hospital mortality rates, etc); rationale for multi-level models in health, education etc
  • Defining Hierarchical Bayesian Models. Three stage models.
  • Benefits from "borrowing strength" using Bayesian random effect models.
  • Measuring model fit for hierarchical models, and procedures for model checking; effective parameters (and DIC)
  • Common conjugate hierarchical models with worked examples

SESSION 2 - Bayesian Hierarchical Models for Meta Analysis
  • Modelling the variance/covariance in Bayesian random effects models. Alternative priors for variances. Winbugs implementation of these priors.
  • Bayesian meta-analysis and pooled estimates in clinical studies and education
  • Different meta-analysis schemes (e.g. beta-binomial, logit-normal for binomial data)

SESSION 3 - Multi-Level and Panel Models
  • Multi-level models (2 and 3 level models for continuous, count and binary responses) and Winbugs implementation to include data input structures.
  • Simple panel models (random intercept, random slope) from a Bayesian perspective.

SESSION 4 - More on Multilevel Models; Hierarchical Bayesian Regression Models
  • Crossed and multivariate and multilevel models
  • Overdispersed regression options for count and proportion data including negative binomial and beta-binomial regression

You will be able to ask questions and exchange comments with the instructors via a private discussion board throughout the course.   The courses take place online at in a series of 4 weekly lessons and assignments, and require about 15 hours/week.  Participate at your own convenience; there are no set times when you must be online. You have the flexibility to work a bit every day, if that is your preference, or concentrate your work in just a couple of days.

For Indian participants accepts registration for its courses at special prices in Indian Rupees through its partner, the Center for eLearning and Training (C-eLT), Pune (

For India Registration and pricing, please visit us at

Call: 020 66009116


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