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.
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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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
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
2021
Aug 20, 2021 to Sep 17, 2021
2022
Aug 19, 2022 to Sep 16, 2022
2023
No classes scheduled at this time.
Prerequisites
The courses listed below are prerequisites for enrollment in this course:
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, Statistical Modeling | Skill: Intermediate | Credit Options: CAP, CEU
Class Start Dates: Jul 23, 2021, Jan 21, 2022
Class Start Dates: Jul 23, 2021, Jan 21, 2022
What Our Students Say
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: https://www.schev.edu/
Please see our knowledge center for more information.
Related Courses
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, Statistical Modeling, Using R | Skill: Intermediate | Credit Options: CAP, CEU
Class Start Dates: Sep 17, 2021
Class Start Dates: Sep 17, 2021
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, Statistical Modeling | Skill: Intermediate, Advanced | Credit Options: CEU
Class Start Dates: Nov 19, 2021, Nov 18, 2022
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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, Statistical Modeling | Skill: Intermediate | Credit Options: CAP, CEU
Class Start Dates: Jul 23, 2021, Jan 21, 2022
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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, Statistical Modeling | Skill: Intermediate, Advanced | Credit Options: CAP, CEU
Class Start Dates: Oct 15, 2021, Oct 14, 2022
Class Start Dates: Oct 15, 2021, Oct 14, 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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