Overview

This course focuses on the principles of Bayesian analysis with the aim to enable participants to apply Bayesian methods on their own research and understand other people's results via Bayesian analysis.

Bayesian analysis is an alternative approach to the statistical techniques that are commonly used throughout most of the research world for the analysis of data. It's core principle stems for the idea that experiments are not abstract devices, hence knowledge existent prior to an experiment should be incorporated formally and openly in the analysis.

Course structure and teaching

This is an online, self-paced course that includes:

• Full electronic notes
• Short lecture videos that follow closely with the notes.
• Accessible materials with alternative text for images, and captions/transcripts for each video.
• Interactive quizzes for each chapter.
• Revision summaries.
• Support will also be available through a forum, where you can ask questions related to the course materials.

Learning outcomes

At the end of the course, delegates should be able to think statistically using Bayesian reasoning and have the tools to implement such reasoning formally into a statistical analysis of data. In particular, delegates will be able to:

• Differentiate the principles of both classical and Bayesian statistical frameworks, and the ad-vantages / disadvantages of each.
• Understand the concept of a probability distribution, including the main established distribu-tions (e.g. the normal distribution) and their parameters.
• Recite the different contributing probability distributions that play a role in Bayes theorem of conditional probabilities.
• Incorporate their prior beliefs into an analysis alongside evidence presented by the collected data.
• Use the results of Bayesian analysis to answer their research question (through prediction, estimation hypothesis testing and credible intervals).
• Have appreciation for the computational complexity of fitting some Bayesian models.

Entry requirements

A basic level of statistical literacy is required as a prerequisite.

It is desirable for the course participants to have basic knowledge of statistics, i.e. notion of statistical inference, p-values and Confidence intervals.

Those who have completed the five-day Introduction to Statistics and Research Methods course run frequently by the Centre for Applied Statistics Courses (CASC) team will be equipped.

Cost and concessions

The standard price is £75.

A 50% discount is available for UCL staff, students, alumni. If you're eligible for a discount, email ich.statscou@ucl.ac.uk before booking to be sent the discount code.

The course is available for free to those associated with the Institute of Child Health or Great Ormond Street Hospital, and SLMS doctoral students. Please also email ich.statscou@ucl.ac.uk to receive a booking code.

Certificates

You can download a certificate of participation once you have completed all the session quizzes.

Find out about other statistics courses

CASC's stats courses are suitable for anyone requiring an understanding of research methodology and statistical analyses. The courses allow non-statisticians to interpret published research and/or undertake their own research studies.

Find out more about CASC's full range of statistics courses.

Course team

Dr Dean Langan

Dean works as a lecturer, jointly based within the School of Life and Medical Sciences (SLMS) and the Centre for Applied Statistics Courses (CASC) at UCL. He has a Bachelor’s degree in Mathematics from University of Liverpool, a Master's degree in Medical Statistics from University of Leicester, and a PhD from University of York for his research in statistical methods for random-effects meta-analysis. He's worked as a statistician on a number of clinical trials related to stroke and myeloma at the Clinical Trials Research Unit in Leeds. His specialist areas include statistical methods for meta-analysis, R programming, clinical trial methodology and research design.

Course information last modified: 21 Mar 2024, 16:17