An Introduction to Bayesian Hierarchical and Multi-level Models

taught by Peter Congdon

Brief Description:

This course extends the Bayesian modeling framework to cover hierarchical models, and to add flexibility to standard Bayesian modeling problems.

Instructor(s):
Level: advanced/intermediate

Who Should Take This Course:

Statistical analysts with some familiarity with Bayesian analysis who want to deepen their skill set in Bayesian modeling.

Dates:
June 22, 2012 to July 20, 2012June 21, 2013 to July 19, 2013
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Registration:
Please read the syllabus tab, noting the prerequisites, text and software requirements.

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An Introduction to Bayesian Hierarchical and Multi-level Models

taught by Peter Congdon

Aim of Course:

This course 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.

Prerequisite(s):

• Intro to Bayes
• Intro to Bayesian Computing
• Recommended, but not required:  Mixed and Hierarchical Linear Models

Students should also have some familiarity with WINBUGS software.

Course Program:

SESSION 1

• 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

• 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 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

• Regression models with a hierarchical structure
• Overdispersed regression options for count and proportion data including negative binomial and beta-binomial regression

HOMEWORK:

Homework in this course consists of short answer questions to test concepts, guided data analysis and modeling problems using software.

Organization of the Course:

This course takes place over the internet at the Institute for 4 weeks. During each course week, you participate at times of your own choosing - there are no set times when you must be online. Course participants will be given access to a private discussion board. In class discussions led by the instructor, you can post questions, seek clarification, and interact with your fellow students and the instructor.

The course typically requires 15 hours per week. At the beginning of each week, you receive the relevant material, in addition to answers to exercises from the previous session. During the week, you are expected to go over the course materials, work through exercises, and submit answers. Discussion among participants is encouraged. The instructor will provide answers and comments, and at the end of the week, you will receive individual feedback on your homework answers.

Credit:
Students come to the Institute for a variety of reasons. As you begin the course, you will be asked to specify your category:

1. You may be interested only in learning the material presented, and not be concerned with grades or a record of completion.
2. You may be enrolled in PASS (Programs in Analytics and Statistical Studies) that requires demonstration of proficiency in the subject, in which case your work will be assessed for a grade.
3. You may require a "Record of Course Completion," along with professional development credit in the form of Continuing Education Units (CEU's).  For those successfully completing the course, 5.0 CEU's and a record of course completion will be issued by The Institute, upon request.

Course Text:

Recommended Reading:  Congdon, P (2003) Applied Bayesian Modelling, chapters, 2,4,6, available through the publisher, Wiley, here.

Wiley typically offers statistics.com customers up to 15% discount on this book (and all other statistics titles): enter the code aff15 in the Promotion Code field when prompted during checkout and click the Apply Discount button. (If you are located in Asia, the web procedure for your location may not accept this discount -- try calling your regional Wiley representative.)

Software:

WINBUGS is used in the course; students should have some familiarity with it prior to taking the course (this can be gained in The Institute's other courses on Bayesian analysis).

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Yes, I want to register for:

An Introduction to Bayesian Hierarchical and Multi-level Models

taught by Peter Congdon

Instructor(s):
Dates:
June 22, 2012 to July 20, 2012June 21, 2013 to July 19, 2013
Course Fee: $499
Academic Rate: $399

Before registering, please read the syllabus tab, noting the prerequisites, text and software requirements. When you click the register button, you will be taken to our secure transaction page.

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