Pre Recorded
Bayesian methods are now increasingly widely in data analysis across most scientific research fields. Given that Bayesian methods differ conceptually and theoretically from their classical statistical counterparts that are traditionally taught in statistics courses, many researchers do not have opportunities to learn the fundamentals of Bayesian methods, which makes using Bayesian data analysis in practice more challenging. The aim of this course is to provide a solid introduction to Bayesian methods, both theoretically and practically. We will teach the fundamental concepts of Bayesian inference and Bayesian modelling, including how Bayesian methods differ from their classical statistics counterparts, and show how to do Bayesian data analysis in practice in R. We begin with a gentle introduction to all the fundamental principles and concepts of Bayesian methods: the likelihood function, prior distributions, posterior distributions, high posterior density intervals, posterior predictive distributions, marginal likelihoods, Bayesian model selection, etc. We will do this using some simple probabilistic models that are easy to understand and easy to work with. We then proceed to more practically useful Bayesian analyses, specifically general linear models. For these analyses, we will use real world data sets, and carry out the analysis using the brms package in R, which is an excellent and powerful package for Bayesian analysis. In this coverage, we will also provide a brief introduction to Markov Chain Monte Carlo methods, although these will be described in more detail in subsequent Bayesian data analysis courses.
This course is aimed at anyone who is interested to learn and apply Bayesian data analysis in any area of science, including the social sciences, life sciences, physical sciences. No prior experience or familiarity with Bayesian statistics is required.
Last Up-Dated – 20:05:2022
Duration – Approx. 15 hours
ECT’s – Equal to 1 ECT’s
Language – English
This course will be largely practical, hands-on, and workshop based. For each topic, there will first be some lecture style presentation, i.e., using slides or blackboard, to introduce and explain key concepts and theories. Then, we will cover how to perform the various statistical analyses using R. Any code that the instructor produces during these sessions will be uploaded to a publicly available GitHub site after each session. For the breaks between sessions, and between days, optional exercises will be provided. Solutions to these exercises and brief discussions of them will take place after each break.
Although not strictly required, using a large monitor or preferably even a second monitor will make the learning experience better, as you will be able to see my RStudio and your own RStudio simultaneously.
All the sessions will be video recorded, and made available immediately on a private video hosting website. Any materials, such as slides, data sets, etc., will be shared via GitHub.
We assume familiarity with inferential statistics concepts like hypothesis testing and statistical significance, and some practical experience with commonly used methods like linear regression, correlation, or t-tests. Most or all of these concepts and methods are covered in a typical undergraduate statistics courses in any of the sciences and related fields.
R experience is desirable but not essential. Although we will be using R extensively, all the code that we use will be made available, and so attendees will just need to copy and paste and add minor modifications to this code. Attendees should install R and RStudio and some R packages on their own computers before the workshops, and have some minimal familiarity with the R environment.
A laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs, Macs, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/.
All the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed, and a full list of required packages will be made available to all attendees prior to the course.
A working webcam is desirable for enhanced interactivity during the live sessions, we encourage attendees to keep their cameras on during live zoom sessions.
Although not strictly required, using a large monitor or preferably even a second monitor will improve he learning experience
PLEASE READ – CANCELLATION POLICY
Cancellations/refunds are accepted as long as the course materials have not been accessed,.
There is a 20% cancellation fee to cover administration and possible bank fess.
If you need to discuss cancelling please contact oliverhooker@prstatistics.com.
If you are unsure about course suitability, please get in touch by email to find out more oliverhooker@prstatistics.com
Day 1
Approx. 6 hours
Topic 1: We will begin with a overview of what Bayesian data analysis is in essence and how it fits into statistics as it practiced generally. Our main point here will be that Bayesian data analysis is effectively an alternativeV school of statistics to the traditional approach, which is referred to variously as the classical, or sampling theory based, or frequentist based approach, rather than being a specialized or advanced statistics topic. However, there is no real necessity to see these two general approaches as being mutually exclusive and in direct competition, and a pragmatic blend of both approaches is entirely possible.
Topic 2: Introducing Bayes’ rule. Bayes’ rule can be described as a means to calculate the probability of causes from some known effects. As such, it can be used as a means for performing statistical inference. In this section of the course, we will work through some simple and intuitive calculations using Bayes’ rule. Ultimately, all of Bayesian data analysis is based on an application of these methods to more complex statistical models, and so understanding these simple cases of the application of Bayes’ rule can help provide a foundation for the more complex cases.
Topic 3: Bayesian inference in a simple statistical model. In this section, we will work through a classic statistical inference problem, namely inferring the number of red marbles in an urn of red and black marbles, or equivalent problems. This problem is easy to analyse completely with just the use of R, but yet allows us to delve into all the key concepts of all Bayesian statistics including the likelihood function, prior distributions, posterior distributions, maximum a posteriori estimation, high posterior density intervals, posterior predictive intervals, marginal likelihoods, Bayes factors, model evaluation of out-of-sample generalization.
Day 2
Approx. 6 hours
Topic 1: We will begin with a overview of what Bayesian data analysis is in essence and how it fits into statistics as it practiced generally. Our main point here will be that Bayesian data analysis is effectively an alternative school of statistics to the traditional approach, which is referred to variously as the classical, or sampling theory based, or frequentist based approach, rather than being a specialized or advanced statistics topic. However, there is no real necessity to see these two general approaches as being mutually exclusive and in direct competition, and a pragmatic blend of both approaches is entirely possible.
Topic 2: Introducing Bayes’ rule. Bayes’ rule can be described as a means to calculate the probability of causes from some known effects. As such, it can be used as a means for performing statistical inference. In this section of the course, we will work through some simple and intuitive calculations using Bayes’ rule. Ultimately, all of Bayesian data analysis is based on an application of these methods to more complex statistical models, and so understanding these simple cases of the application of Bayes’ rule can help provide a foundation for the more complex cases.
Topic 3: Bayesian inference in a simple statistical model. In this section, we will work through a classic statistical inference problem, namely inferring the number of red marbles in an urn of red and black marbles, or equivalent problems. This problem is easy to analyse completely with just the use of R, but yet allows us to delve into all the key concepts of all Bayesian statistics including the likelihood function, prior distributions, posterior Distributions, maximum a posteriori estimation, high posterior density intervals, posterior predictive intervals, marginal likelihoods, Bayes factors, model evaluation of out-of-sample generalization.
Works At
Senior Lecturer, Psychology Department, Nottingham Trent University, England
Personal website
Google Scholar
Mark Andrews is a Senior Lecturer in the Psychology Department at Nottingham Trent University in Nottingham, England. Mark is a graduate of the National University of Ireland and obtained an MA and PhD from Cornell University in New York. Mark’s research focuses on developing and testing Bayesian models of human cognition, with particular focus on human language processing and human memory. Mark’s research also focuses on general Bayesian data analysis, particularly as applied to data from the social and behavioural sciences. Since 2015, he and his colleague Professor Thom Baguley have been funded by the UK’s ESRC funding body to provide intensive workshops on Bayesian data analysis for researchers in the social sciences.