Bayesian Regression Modeling via MCMC Techniques

taught by Peter Congdon

Brief Description:

This course covers the application of Markov Chain Monte Carlo techniques (MCMC) to Bayesian statistical modeling using WinBUGS software.

Instructor(s):
Level: Advanced-Intermediate

Who Should Take This Course:

Statisticians and analysts who need to build statistical models of data.

Dates:
November 16, 2012 to December 14, 2012
mcmc Click here to be reminded of future sessions of this course.

Registration:
Please read the syllabus tab, noting the prerequisites, text and software requirements.

Register Online -$499
Register Online -$399 (you must be affiliated with a college, university or high school)

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Bayesian Regression Modeling via MCMC Techniques

taught by Peter Congdon

Aim of Course:

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

This course is a core requirement or elective in the following Program(s) in Analytics and Statistical Studies (PASS):

• Environmental Science

Prerequisite(s):

• Introduction to Bayesian Statistics
• An Introduction to Bayesian Computing and Techniques
• Generalized Linear Models

Course Program:

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

HOMEWORK:

Homework in this course consists of short answer questions to test concepts and guided data analysis 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:

The required text for this course is Bayesian Statistical Modeling, 2nd edition, by Peter Congdon, and it can be ordered from Wiley by clicking 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.). The text is also available as an "e-book".

PLEASE ORDER YOUR COPY IN TIME FOR THE COURSE STARTING DATE.

Software:

Selected R programs will be used in week 1, but the primary program used will be the freeware WinBUGS program for Gibbs Sampling; click here for more information.

(We recommend that you download and install prior to the course start date.)

Though the R programs used in week 1 will not require a high degree of familiarity with R, if you want to use R with this course, you should have some prior experience and facility with it. Help from the TA will be available, but limited.

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Bayesian Regression Modeling via MCMC Techniques

taught by Peter Congdon

Instructor(s):
Dates:
November 16, 2012 to December 14, 2012
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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