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ONLINE COURSE – Bayesian Approaches to Regression and Mixed Effects Models using R and brms (BARM01) This course will be delivered live

26 May 2021 - 27 May 2021


This course will now be delivered live by video link in light of travel restrictions due to the COVID-19 (Coronavirus) outbreak.

This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.

TIME ZONE – UK local time (GMT+0) – however all sessions will be recorded and made available allowing attendees from different time zones to follow a day behind with an additional 1/2 days support after the official course finish date (please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you).

Course Overview:

Bayesian methods are now increasingly widely used for data analysis based on linear and generalized linear models,
and multilevel and mixed effects models. The aim of this course is to provide a solid introduction to Bayesian
approaches to these topics using R and the brms package. Ultimately, in this course, we aim to show how Bayesian
methods provide a very powerful, flexible, and extensible approach to general statistical data analysis. We begin by
covering Bayesian approaches to linear regression. We will compare and contrast, in both practical and theoretical
terms, the Bayesian approach and classical approach to linear regression. This will allow us to easily identify the major
similarities and major differences, both in terms of concepts and practice, between the Bayesian and classical
approaches. We will then proceed to Bayesian approaches to generalized linear models, including binary logistic
regression, ordinal logistic regression, Poisson regression, zero-inflated models, etc. In this coverage, we will see the
very wide range of models to which Bayesian methods can be easily applied. Finally, we will cover Bayesian approaches
to multilevel and mixed effects models. Here again, we will see how Bayesian methods allow us to easily extend
traditionally used methods like linear and generalized linear mixed effects models. We will also see how Bayesian
methods allow us to control model complexity and solve algorithmic problems (e.g. model convergence problems) that
can plague classical approaches to multilevel and mixed effects models. Throughout this course, we will be using, via
the brms package, Markov Chain Monte Carlo (MCMC) methods. However, full technical details of MCMC will will be
described here, but will be provided in subsequent Bayesian data analysis courses.


Intended Audience

This course is aimed at anyone who is in interested in using Bayesian approaches to regression, multilevel, and mixed
effects models in any area of science, including the social sciences, life sciences, physical sciences. No prior experience
or familiarity with Bayesian statistics is required.

Venue – Delivered remotely

Time zone – GMT+0

Availability – TBC

Duration – 2 days

Contact hours – Approx. 15 hours

ECT’s – Equal to 1 ECT’s

Language – English

PLEASE READ – CANCELLATION POLICY: Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.

Dr. Mark Andrews
Teaching Format
Course Programme


26 May 2021
27 May 2021
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Delivered remotely (United Kingdom)
Western European Time, United Kingdom + Google Map


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