Introduction to MCMC and Bayesian Regression via rstan

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.

In this course, students learn how to apply Markov Chain Monte Carlo techniques (MCMC) to Bayesian statistical modeling using R and rstan.

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Overview

In this course, students learn how to apply Markov Chain Monte Carlo techniques (MCMC) to Bayesian statistical modeling using R and rstan. Topics covered include Gibbs sampling and the Metropolis-Hastings method. Participants also learn how to implement linear regression (normal and t errors), Poisson regression, binary/binomial regression, and ordinal regression.

Learning Outcomes

Students who complete this course will learn how to apply Markov Chain Monte Carlo (MCMC) techniques using R and rstan. You will be introduced to coding for rstan and learn Bayesian methods for both linear  and discrete data regressions.

  • Install rstan and write code in it
  • Implement rstan programs in R
  • Specify priors and likelihoods to define a model
  • Implement a linear regression model in rstan
  • Implement logistic, Poisson, ordinal and weighted regression in rstan

Who Should Take This Course

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

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 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

Week 2

Introduction to Coding for rstan and Running An Analysis

  • Using rstan, coding principles
  • R implementations of rstan programs
  • Sampling from standard densities, distributional and target + options
  • Specifying priors and likelihoods to define a model
  • Posterior summaries

Week 3

Bayesian Methods for Linear Regression

  • Linear regression model in rstan
  • Setting priors on regression coefficients and residual variances
  • Extending the Normal linear model (outliers, heteroscedasticity)
  • Shrinkage Priors

Week 4

Bayesian Methods for Discrete Data Regression

  • Logistic regression for binary and binomial responses; using other links
  • Poisson regression
  • Ordinal Regression
  • Weighted regression

Class Dates

2019

No classes scheduled at this time.

2020

Apr 17, 2020 to May 15, 2020

2021

Apr 16, 2021 to May 14, 2021

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Prerequisites

Participants should have had some exposure to Bayesian computing such as that provided in our Introduction to Bayesian Computing course, and familiarity with generalized linear models, such as that provided in Generalized Linear Models course.

Generalized Linear Models

This course will explain the theory of generalized linear models (GLM), outline the algorithms used for GLM estimation, and explain how to determine which algorithm to use for a given data analysis.
Topic: Statistics, Statistical Modeling | Skill: Intermediate, Advanced | Credit Options: CEU
Class Start Dates: Apr 10, 2020

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.
Topic: Statistics, Bayesian | Skill: Intermediate | Credit Options: CAP, CEU
Class Start Dates: Feb 21, 2020, Aug 21, 2020

What Our Students Say​

This was a highly interactive course with a first rank expert in his topic who, fortunately, was also good communicator who likes teaching.

John Steward
Welsh Cancer Intelligence & Surveillance Unit

This course is well designed and the course materials are simply awesome! The instructor is very knowledgeable in the field and has tons of examples from their website, which is a huge benefit. The focus on programming and practical tips, as opposed to theoretical details, is also a great benefit.

Victor Lo
Vice President, Data Science, Fidelity Investments

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

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Topic: Statistics, Bayesian | Skill: Intermediate | Credit Options: CAP, CEU
Class Start Dates: Mar 20, 2020, Sep 18, 2020

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.
Topic: Statistics, Bayesian | Skill: Intermediate | Credit Options: CAP, CEU
Class Start Dates: Feb 21, 2020, Aug 21, 2020

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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
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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 | Skill: Intermediate | Credit Options: 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

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

In addition to assigned readings, this course also has supplemental readings available online, end of course data modeling project, and example software codes.

Course Text

The required text for this course is Bayesian Statistical Modeling, 2nd edition, by Peter Congdon.

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

Software

Selected R programs will be used in week 1, but the primary program used will be the freeware rstan, which can be downloaded from the R-Project.

Though the R programs used in week 1 will not require a high degree of familiarity with R, if you want to continue to use R with the entire course, you should have some prior experience and facility with it.

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 MCMC and Bayesian Regression via rstan
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