Bayesian Regression Modeling via MCMC Techniques

In 1946, the Polish mathematician Stanislaw Ulam lay in bed with a fever, and passed the time attempting to calculate the probability that you would win at solitaire.  He gave up, but decided a better way would be to actually play the game 100 times, and find out how often you were successful.  This was his inspiration for what he termed the Monte Carlo method, after the casino where his uncle gambled.

Monte Carlo simulation methods have many success stories, among them the blossoming of Bayesian statistics via Markov Chain Monte Carlo (MCMC) computations.  Our online course "Bayesian Regression Modeling via MCMC Techniques" will be offered by Prof. Peter Congdon, a noted author in this area. For more details please visit at http://www.statistics.com/MCMC/.

Aim of the course:

Participants in "Bayesian Regression Modeling Via MCMC will learn how to apply Markov Chain Monte Carlo techniques to Bayesian statistical modeling using WINBUGS and R software. Topics covered include Gibbs sampling and the Metropolis-Hastings method.  Participants will learn how to implement linear regression (normal and t errors), poisson and loglinear regression, and binary/binomial regression using WinBUGS. 

Who Should Take This Course:
Statisticians and analysts who need to build statistical models of data.

Course Program:

Course outline: The course is structured as follows

SESSION 1: Using Markov Chain Monte Carlo

• Monte Carlo vs MCMC
• Estimating parameters and probabilities from complex models
• Sampling from random variables
• Gibbs sampling & full conditional densities
• Convergence
• Metropolis-Hastings method

 

SESSION 2:

• Sampling from standard densities
• Specifying priors and likelihoods
• Assessing convergence
• Estimating parameters, probabilities and other model based quantities: Case Studies
• Posterior summaries

 

SESSION 3: Linear Regression Modeling in WinBUGS

• Linear regression model in WinBUGS
• Setting priors on regression coefficients and residual variances
• Predictor selection
• Extending the Normal linear model (outliers, heteroscedasticity)

 

SESSION 4: General Linear Modeling in WinBUGS

• Logistic regression for binary and binomial responses; using other links
• Poisson regression
• Latent data approach for binary regression
• Loglinear models for contingency tables

Dr. Peter Congdon is a Research Professor in Quantitative Geography and Health Statistics at Queen Mary University of London. He is the author of "Bayesian Statistical Modeling," "Applied Bayesian Modeling," and "Bayesian Models for Categorical Data," all published by Wiley, as well as numerous articles in peer-reviewed journals.  His research interests include spatial data analysis, Bayesian statistics, latent variable models and epidemiology.  Participants can ask questions and exchange comments with Dr. Congdon via a private discussion board throughout the period.

You will be able to ask questions and exchange comments with Dr. Peter Congdon 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.

For India Registration and pricing, please visit us at www.india.statistics.com.

For More details contact at

Email: info@c-elt.com

Call: 020 66009116

Websites:

www.india.statistics.com

www.c-elt.com