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 http://www.statistics.com/bayesian-hierarchical-models.
"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 statistics.com 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 statistics.com accepts registration for its courses at special
prices in Indian Rupees through its partner, the Center for eLearning and
Training (C-eLT), Pune (www.c-elt.com).
Call: 020 66009116
Websites:
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