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Introduction to Advanced Bayesian Multilevel Modeling - Workshop 3

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Course Information

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This workshop focuses on advanced probabilistic modeling in Bayesian data analysis, and in particular, Bayesian data analysis using multilevel regression models.

Booking Option Fee
Full Workshop Fee £20.00
Full Workshop Fee - Postgraduate student rate £10.00


Course Code

ESRC Workshop 3

Course Date

27th September 2017

Places Available

Course Fee

Course Description

Time: 9 am - 5 pm

Location: Room 5108, Chaucer building, Nottingham Trent University, NG1 5LT

Workshop content

Given that Bayesian inference in almost all advanced probabilistic models is analytically intractable, the initial focus of this workshop will be on the theory and practice of Monte Carlo, and particularly Markov Chain Monte Carlo (MCMC), methods for drawing random samples from posterior probability distributions. Despite its relatively advanced nature, it is important to emphasise the MCMC methods are not a niche topic in Bayesian data analysis. They are at the heart of almost all modern applications of Bayesian data analysis, and they are what have allowed Bayesian methods to become so influential in modern statistics. In addition, this workshop will also focus heavily on the nature and practice of multilevel regression modeling.

Multilevel models are becoming increasingly prevalent in data analysis in psychology and the social sciences. Inference in multilevel models presents major challenges for classical methods, while inference using Bayesian methods is always possible in principle. The practical activities in this workshop will largely focus on the use of the BUGS/JAGS macro language. This is an extremely powerful general tool for Bayesian data analysis as it allows for MCMC based inference in arbitrary probabilistic models. We will introduce BUGS/JAGS by way of relatively simple models, followed by in depth application to multilevel models.


The prerequisites for this workshop are fulfilled by the contents of workshops one and two. Specifically, we assume both practical and theoretical competence with the simpler analytically tractable Bayesian models such as Bayesian t tests, ANOVA and linear regression. We will also assume that attendees are familiar with the R statistical computing environment.

Learning outcomes

On completion of this workshop, we expect attendees to be able to confidently perform and understand Bayesian data analysis when applied to real-world complex data sets of the kind routinely encountered in research in psychology and social sciences. They will also be familiar with the general principals of MCMC methods and how these are an essential practical tool for Bayesian inference in complex statistical models.

Indicative reading

Kruschke, J. K. (2011). Doing Bayesian data analysis. Burlington, MA: Academic Press.

Gelman, A., Hill, J. (2007). Data analysis using regression and multilevel/hierarchical models. New York, NY: Cambridge University Press.


Bursaries are available to apply for, to assist with costs associated with attending this workshop. Please see the More Info tab for further information. Please note that only PhD students who attend UK HE institutions are eligible to apply for this bursary.