Introduction to Bayesian Computing and Techniques

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.

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

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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.

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.

Instructors

dr-peter-congdon

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.
See Instructor Bio

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

2019

Jul 5, 2019 to Aug 2, 2019

2020

Feb 21, 2020 to Mar 20, 2020

Aug 21, 2020 to Sep 18, 2020

2021

No classes scheduled at this time.

Send me reminder for next class

Prerequisites

The courses listed below are prerequisites for enrollment in this course:

Introduction to Bayesian Statistics

This course will teach you the basic ideas of Bayesian Statistics: how to perform Bayesian analysis for a binomial proportion, a normal mean, the difference between normal means, the difference between proportions, and for a simple linear regression model.
Topic: Statistics, Bayesian | Skill: Intermediate | Credit Options: CAP, CEU
Class Start Dates: Jan 17, 2020, Jul 3, 2020

What Our Students Say​

I would recommend this class to anybody (with a basis in probability theory and calculus) who wishes to get acquainted with bayesianism.

Jimmy Bourque
Exam Developer at Royal College of Physicians and Surgeons of Canada

I learned a great deal from this course. I thought that the instructor, Dr. Congdon, prepared excellent lessons for the course.  Dr. Congdon's responses to the questions on the discussion board were clear and very helpful. The TA for this course was also excellent.

Margaret Palmisano

Frequently Asked Questions

Can I transfer or withdraw from a course?

We have a flexible transfer and withdrawal policy that recognizes circumstances may arise to prevent you from taking a course as planned. You may transfer or withdraw from a course under certain conditions.

  • Students are entitled to a full refund if a course they are registered for is canceled.
  • You can transfer your tuition to another course at any time prior to the course start date or the drop date, however a transfer is not permitted after the drop date.
  • Withdrawals on or after the first day of class are entitled to a percentage refund of tuition.

Please see this page for more information.

Who are the instructors at the Institute?

The Institute has more than 60 instructors who are recruited based on their expertise in various areas in statistics. Our faculty members are:

  • Authors of well-regarded texts in their area;
  • Advisory board members;
  • Senior faculty; and
  • Educators who have made important contributions to the field of statistics or online education in statistics.

The majority of our instructors have more than five years of teaching experience online at the Institute.

Please visit our faculty page for more information on each instructor at The Institute for Statistics Education.

Please see our knowledge center for more information.

What type of courses does the Institute offer?

The Institute offers approximately 80 courses each year. Topics include basic survey courses for novices, a full sequence of introductory statistics courses, bridge courses to more advanced topics. Our courses cover a range of topics including biostatistics, research statistics, data mining, business analytics, survey statistics, and environmental statistics.

Please see our course search or knowledge center for more information.

Do your courses have for-credit options?

Our courses have several for-credit options:

  • Continuing education units (CEU)
  • College credit through The American Council on Education (ACE CREDIT)
  • Course credits that are transferable to the INFORMS Certified Analytics Professional (CAP®)

Please see our knowledge center for more information.

Is the Institute for Statistics Education certified?

The Institute for Statistics Education is certified to operate by the State Council of Higher Education for Virginia (SCHEV). For more information visit: http://www.schev.edu

Please see our knowledge center for more information.

Visit our knowledge base and learn more.

FAQs + Knowledge Base

Related Courses

Bayesian Statistics in R

This course will teach you how to specify and run Bayesian modeling procedures using regression models for continuous, count and categorical data Using R and the associated R package JAGS.
Topic: Statistics, Bayesian | Skill: Intermediate | Credit Options: CAP, CEU
Class Start Dates: Mar 20, 2020, Sep 18, 2020

Introduction to Bayesian Hierarchical and Multi-level Models

This course will teach you how to extend the Bayesian modeling framework to cover hierarchical models and to add flexibility to standard Bayesian modeling problems.
Topic: Statistics, Bayesian | Skill: Intermediate, Advanced | Credit Options: CEU
Class Start Dates: May 15, 2020

Introduction to Bayesian Statistics

This course will teach you the basic ideas of Bayesian Statistics: how to perform Bayesian analysis for a binomial proportion, a normal mean, the difference between normal means, the difference between proportions, and for a simple linear regression model.
Topic: Statistics, Bayesian | Skill: Intermediate | Credit Options: CAP, CEU
Class Start Dates: Jan 17, 2020, Jul 3, 2020

Introduction to MCMC and Bayesian Regression via rstan

In this course, students learn how to apply Markov Chain Monte Carlo techniques (MCMC) to Bayesian statistical modeling using R and rstan.
Topic: Statistics, Bayesian | Skill: Intermediate, Advanced | Credit Options: CAP, CEU
Class Start Dates: Apr 17, 2020, Apr 16, 2021, Apr 15, 2022

Additional Course Information

Organization of Course

This course takes place online 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.

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.

Time Requirements

This is a 4-week course requiring 10-15 hours per week of review and study, at times of your choosing.

Homework

The homework in this course consists of short answer questions to test concepts, guided exercises in writing code, and guided data analysis problems using software.

In addition to assigned readings, this course also has supplemental readings available online.

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.

Please order a copy of your course textbook prior to course start date.

Software

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

Software Uses and Descriptions | Available Free Versions
To learn more about the software used in this course, or how to obtain free versions of software used in our courses, please read our knowledge base article “What software is used in courses?” 

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
Contact us to get information on group rates.

Discounts
Academic affiliation?  In most courses you are eligible for a discount at checkout.

New to Statistics.com?  Click here for a special introductory discount code.  

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.

Options for Credit and Recognition

This course is eligible for the following credit and recognition options:

No Credit
You may take this course without pursuing credit or a record of completion.

Mastery or Certificate Program Credit
If you are enrolled in mastery or certificate program that requires demonstration of proficiency in this subject, your course work may be assessed for a grade.

CEUs and Proof of Completion
If you require a “Record of Course Completion” along with professional development credit in the form of Continuing Education Units (CEU’s), upon successfully completing the course, CEU’s and a record of course completion will be issued by The Institute upon your request.

INFORMS-CAP
This course is recognized by the Institute for Operations Research and the Management Sciences (INFORMS) as helpful preparation for the Certified Analytics Professional (CAP®) exam and can help CAP® analysts accrue Professional Development Units to maintain their certification.

Supplemental Information

There is no supplemental content for this course.

Miscellaneous

There is no additional information for this course.

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Introduction to Bayesian Computing and Techniques
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