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.
Overview
Complex sample designs such as stratified cluster sampling make it possible to extract maximum information at minimum cost, but they are typically harder to work with than simple random samples. How do you analyze the resulting data – in particular, how do you determine margins of error? This course teaches you how to estimate variances when analyzing survey data from complex samples, and also how to fit linear and logistic regression models to complex sample survey data.
- Intermediate, Advanced
- 4 Weeks
- Expert Instructor
- Tuiton-Back Guarantee
- 100% Online
- TA Support
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.
Our Instructors
Dr. Peter Congdon
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
Prerequisites
Students should also have some familiarity with WinBUGS/OPENBUGS software.
Introduction to Bayesian Computing and Techniques
- Skill: Intermediate, Advanced
- Credit Options: CEU
Introduction to R Programming
- Skill: Intermediate, Advanced
- Credit Options: CEU
Class Dates
2022
Instructors:
2023
Instructors:
2024
Instructors:
The Statistics.com courses have helped me a lot, pushing me to the limit and making me learn much more than I expected I could. The knowledge I gained I could immediately leverage in my job … then eventually led to landing a job in my dream company – Amazon.
Karolis Urbonas
This program has been a life and work game changer for me. Within 2 weeks of taking this class, I was able to produce far more than I ever had before.
Susan Kamp
The material covered in the Analytics for Data Science Certificate will be indispensable in my work. I can’t wait to take other courses. Great work!
Stephen McAllister
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
This is the best online course I have ever taken. Very well prepared. Covers a lot of real-life problems. Good job, thank you very much!
Elena Rose
The more courses I take at Statistics.com, the more appreciation I have for the smart approach, quality of instructors, assistants, admin and program. Well done!
Leonardo Nagata
This course greatly benefited me because I am interested in working in AI. It has given me solid foundational knowledge…After completing this last course, I feel I have gained valuable skills that will enhance my employability in Data Science, opening up diverse career opportunities.
Richard Jackson
Frequently Asked Questions
-
What is your satisfaction guarantee and how does it work?
-
Can I transfer or withdraw from a course?
-
Who are the instructors at Statistics.com?
Visit our knowledge base and learn more.
Register For This Course
Introduction to Bayesian Hierarchical and Multi-level Models
Additional Information
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).
Supplemental Information
Literacy, Accessibility, and Dyslexia
At Statistics.com, we aim to provide a learning environment suitable for everyone. To help you get the most out of your learning experience, we have researched and tested several assistance tools. For students with dyslexia, colorblindness, or reading difficulties, we recommend the following web browser add-ons and extensions:
Chrome
- Color Enhancer (for colorblindness)
- HelperBird (for colorblindness, dyslexia, and reading difficulties)
Firefox
- Mobile Dyslexic
- Color Vision Simulation (native accessibility feature)
- Other native accessibility features instructions
Safari
- Navidys (for colorblindness, dyslexia, and reading difficulties)
- HelperBird for Safari (for colorblindness, dyslexia, and reading difficulties)
Register For This Course
Introduction to Bayesian Hierarchical and Multi-level Models