Introduction to Bayesian Hierarchical and Multi-level Models

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.

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.

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Overview

This course extends the Bayesian modeling framework to cover hierarchical models and to add flexibility to standard Bayesian modeling problems. The course is designed for analysts with some familiarity with Bayesian analysis who want to deepen their skill set in Bayesian modeling. Students will learn how to define three-stage hierarchical models, how to implement them using Winbugs in multilevel, meta-analytic and regression applications, and how to assess goodness of fit. Continuous, count and binary outcomes are also covered.

Learning Outcomes

Students who complete this course will learn how to define Bayesian hierarchical models, hierarchical models for meta analysis, and hierarchical Bayesian regression models. They will explore computing options (BUGS and R) and Winbugs implementation for various Bayesian analyses. 

  • Specify 3-stage Bayesian hierarchical model
  • Measure model fit and check parameters
  • Model the variance/covariance in Bayesian random effects models
  • Apply Bayesian hierarchical models to meta-analysis
  • Specify multi-level and panel models
  • Manage overdispersion for count and proportion data

Who Should Take This Course

Statistical analysts with some familiarity with Bayesian analysis who want to deepen their skill set in Bayesian modeling.

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

Defining Bayesian Hierarchical Models

  • Overview of application contexts: meta-analysis to summarise accumulated evidence; comparisons of related units (e.g. "league table comparisons" of exam results, hospital mortality rates, etc); rationale for multi-level models in health, education etc
  • Defining Hierarchical Bayesian Models. Three stage models.
  • Benefits from "borrowing strength" using Bayesian random effect models.
  • Measuring model fit for hierarchical models, and procedures for model checking; effective parameters (and DIC)
  • Common conjugate hierarchical models with worked examples
  • Computing options (BUGS and R)

Week 2

Bayesian Hierarchical Models for Meta Analysis

  • Modelling the variance/covariance in Bayesian random effects models. Alternative priors for variances. Winbugs implementation of these priors.
  • Bayesian meta-analysis and pooled estimates in clinical studies and education
  • Different meta-analysis schemes (e.g. beta-binomial, logit-normal for binomial data)

Week 3

Multi-Level and Panel Models

  • Multi-level models (2 and 3 level models for continuous, count and binary responses) and Winbugs implementation to include data input structures.
  • Simple panel models (random intercept, random slope) from a Bayesian perspective.

Week 4

More on Multi-level Models; Hierarchical Bayesian Regression Models

  • Crossed and multivariate and multilevel models
  • Overdispersed regression options for count and proportion data including negative binomial and beta-binomial regression

Class Dates

2020

May 15, 2020 to Jun 12, 2020

2021

No classes scheduled at this time.

2022

No classes scheduled at this time.

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Prerequisites

Students should also have some familiarity with WinBUGS/OPENBUGS software. 

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 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

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, Statistical Modeling | Skill: Intermediate | Credit Options: CAP, CEU
Class Start Dates: Jul 3, 2020

R Programming – Introduction Part 1

This course provides an easy introduction to programming in R.
Topic: Data Science, Using R | Skill: Introductory | Credit Options: ACE, CAP, CEU
Class Start Dates: May 15, 2020, Sep 11, 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

I would contend that this Bayesian Computing class (and other Statistics.com classes I've taken) has extremely good value. I have gotten so much education for relatively little expense. the assistant teacher's feedback was extremely helpful.

Richard Coshow
QDNR

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, Statistical Modeling, Using R | 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

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, Statistical Modeling | Skill: Intermediate | Credit Options: CAP, CEU
Class Start Dates: 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, Statistical Modeling | 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

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

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

Course Text

Recommended Reading:  Congdon, P (2003) Applied Bayesian Modelling

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.

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 Hierarchical and Multi-level Models
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