Bayesian Statistics in R

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.

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.

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Overview

This course is designed for analysts who are familiar with R and Bayesian statistics at the introductory level, and need to incorporate Bayesian methods into statistical models. Using R and RJAGS, you will learn how to specify and run Bayesian modeling procedures using regression models for continuous, count and categorical data including: linear regression, Poisson, logit and negative binomial regression, and ordinal regression.

Learning Outcomes

After taking this course you will be able to install and run RJAGS, a program for Bayesian analysis within R.  You will learn how to specify and run Bayesian modeling procedures using regression models for continuous, count and categorical data.

  • Write code in rjags
  • Specify models for linear regression
  • Specify models for count, binary and binomial data
  • Incorporate categorical predictors into models
  • Implement algorithms to select predictors

Who Should Take This Course

You should take this course if you are familiar with R and with Bayesian statistics at the introductory level, and work with or interpret statistical models and need to incorporate Bayesian methods. Analysts who need to incorporate their work into real-world decisions, as opposed to formal statistical inference for publication, will be especially interested. This includes business analysts, environmental scientists, regulators, medical researchers, and engineers. 

In this course you will learn both BUGS coding and how to integrate it into R.  If you are not familiar with BUGS, and want to take the time to learn BUGS first, consider taking the optional prerequisite listed below.

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

Using RJAGS for Bayesian inference in R: Introductory Ideas and Programming Considerations

  • Basic Principles of Bayesian Inference and MCMC Sampling
  • R and RJAGS for Bayesian inference. Initial values, posterior summaries, checking convergence.
  • JAGS and BUGS programming Syntax, with simple applications

Week 2

Linear Regression with RJAGS

  • Specifying Models
  • Specifying Priors on Regression Coefficients and Residual Variances
  • Posterior Summarisation in R

Week 3

Regression for Count, Binary, and Binomial Data

  • Poisson Regression
  • Logit and Probit Regression
  • Negative Binomial Regression

Week 4

Other Regression Techniques

  • Ordinal and multinomial regression
  • Categorical predictors
  • Predictor selection

Class Dates

2019

Sep 19, 2019 to Oct 18, 2019

2020

Mar 20, 2020 to Apr 17, 2020

Sep 18, 2020 to Oct 16, 2020

2021

No classes scheduled at this time.

Send me reminder for next class

Prerequisites

Recommended

We recommended, but do not require as eligibility to enroll in this course, an understanding of the material covered in these following courses.

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​

The material covered here will be indispensable in my work. I can't wait to take other courses. Great work!

Stephen McAllister
Ottawa Public Library

I learned more in the past 6 weeks than I did taking a full semester of statistics in college, and 10 weeks of statistics in graduate school. Seriously.

Amir Aminimanizani
MRR Pharmacy Consulting, Inc

Frequently Asked Questions

What is your satisfaction guarantee and how does it work?

We offer a “Student Satisfaction Guarantee​” that includes a tuition-back guarantee, so go ahead and take our courses risk free. That’s our commitment to student satisfaction. Students may cancel, transfer, or withdraw from a course under certain conditions. If you’re not satisfied with a course, you may withdraw from the course and receive a tuition refund.

Please see our knowledge center for more information.

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.

Visit our knowledge base and learn more.

FAQs + Knowledge Base

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Topic: Statistics, Bayesian | Skill: Intermediate | Credit Options: CAP, CEU
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Introduction to Bayesian Hierarchical and Multi-level Models

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Topic: Statistics, Bayesian | Skill: Intermediate, Advanced | Credit Options: CEU
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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
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Topic: Data Science, Using R | Skill: Introductory | Credit Options: ACE, CAP, CEU
Class Start Dates: Jan 10, 2020, May 15, 2020, Sep 11, 2020

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This course is a continuation of the introduction to R programming.
Topic: Data Science, Using R | Skill: Introductory, Intermediate | Credit Options: ACE, CAP, CEU
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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.

This course has example software codes and supplemental readings available online, and has an end-of-course project.

Course Text

The BUGS Book – A Practical Introduction to Bayesian Analysis, David Lunn et al. CRC Press (2012).

Note: This book is an excellent guide to BUGS. It is not specifically about R, but all required instruction about R coding will be provided in the course materials. If you are already well familiar with BUGS and have your own reference, you may not need this book.

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

Software

This course uses the following software applications: 

The course will focus on use of RJAGS. An rjags implementation in R rests crucially on coding in JAGS, which is virtually identical to BUGS.

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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Bayesian Statistics in R
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