# Introduction to Bayesian Computing and Techniques

This course will teach you how to apply Markov Chain Monte Carlo techniques (MCMC) to Bayesian statistical modeling using WinBUGS software.

## Overview

In this course for statistical analysts and consultants who make decisions using domain-specific information, students learn why Bayesian computing has gained wide popularity, and how to apply Markov Chain Monte Carlo techniques (MCMC) to Bayesian statistical modeling. Participants will use the BUGS package (WinBUGS/OPENBUGS) to estimate parameters of standard distributions, and implement simple regression models.

• Intermediate
• 4 Weeks
• Expert Instructor
• Tuiton-Back Guarantee
• 100% Online
• TA Support

## Learning Outcomes

Students who complete this course will gain a basic understanding of MCMC and the benefits of Bayes methods. They will explore how to build Bayesian models using BUGS, main elements of Bayesian posteriors, and commonly used Bayesian regression models.

• Explain the benefits of Bayesian methods, and the basic idea of MCMC
• Install BUGS software and be able to write BUGS code
• Conduct Bayesian regression
• Work with 3 standard schemes for prior and posterior distributions
• Conduct posterior summaries and tests
• Make predictons from models

## Who Should Take This Course

Statistical analysts and consultants who need to make decisions (or advise decision-makers) via a process that incorporates domain-specific information — not simply abstract and arbitrary statistical rules.

## Our Instructors

#### Dr. Peter Congdon

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.

## Course Syllabus

### Week 1

Introduction to Bayesian MCMC

• Basic ideas of MCMC
• Benefits of Bayes methods
• Priors and Prior Informativeness
• Important distributions in Bayesian analysis
• Introduction to three standard schemes: (normal data, normal prior; binomial data, beta prior; poisson data, gamma prior)

### Week 2

Bayesian Programming in BUGS

• BUGS syntax and programs, data inputs, convergence checks, obtaining summaries

### Week 3

Bayesian Posteriors

• Main elements of posterior summarization
• Tests on parameters or parameter collections (posterior probability tests)
• Model predictions

### Week 4

Bayesian Regression

• Commonly-used regression models (normal, binary & binomial, poisson)

## Class Dates

### 2024

08/23/2024 to 09/23/2024
Instructors: Dr. Peter Congdon

### 2025

08/22/2025 to 09/19/2025
Instructors: Dr. Peter Congdon

## Register For This Course

Introduction to Bayesian Computing and Techniques

#### Course Text

Extensive course notes are provided, and, particularly for those focusing on the implementation of techniques in Winbugs, the course may be followed without purchasing a text.

For a more in-depth look at the underlying concepts, and for those who require a book for reference, we recommend A First Course in Bayesian Statistical Methods, by Peter Hoff.

If you already have Bayesian Modeling Using WinBUGS, by I. Ntzoufras (2008, Wiley), that book is also a useful companion to this course, especially for Lesson 2.

#### Software

The course will be based on the freeware BUGS package (WinBUGS/OPENBUGS).

#### Course Fee & Information

Enrollment
Courses may fill up at any time and registrations are processed in the order in which they are received. Your registration will be confirmed for the first available course date unless you specify otherwise.

Transfers and Withdrawals
We have flexible policies to transfer to another course or withdraw if necessary.

Group Rates

Discounts
Academic affiliation?  In most courses you are eligible for a discount at checkout. Use promo code ACADEMIC where prompted during registration.

Invoice or Purchase Order
Add \$50 service fee if you require a prior invoice, or if you need to submit a purchase order or voucher, pay by wire transfer or EFT, or refund and reprocess a prior payment.

#### Literacy, Accessibility, and Dyslexia

At Statistics.com, we aim to provide a learning environment suitable for everyone. To help you get the most out of your learning experience, we have researched and tested several assistance tools. For students with dyslexia, colorblindness, or reading difficulties, we recommend the following web browser add-ons and extensions:

Chrome

Firefox

Safari

• Navidys (for colorblindness, dyslexia, and reading difficulties)
• HelperBird for Safari (for colorblindness, dyslexia, and reading difficulties)

## Register For This Course

Introduction to Bayesian Computing and Techniques