Introduction to Bayesian Statistics for Social Scientists - Online

Description:

The purpose of this course is to familiarise students with the basic concepts of Bayesian theory. It is designed to provide an introduction to the principles, methods, and applications of Bayesian statistics. Bayesian statistics offers a powerful framework for data analysis and inference, allowing for the incorporation of prior knowledge and uncertainty in a coherent and systematic manner.

Throughout this course, we will cover key concepts such as Bayes' theorem, prior and posterior distributions, likelihood functions, and the fundamental differences between Bayesian and frequentist approaches. You will learn to formulate and estimate statistical models, update beliefs using new data, and make informed decisions based on the posterior probabilities generated through Bayesian inference.

By the end of this course, you will possess the necessary skills to perform Bayesian data analysis, interpret results, and apply Bayesian methods in various contexts.

The course covers: 

• The Basics of Probability

• Bayes’ Theorem and Bayesian inference

• Probability Functions

• Bayesian Conjugates

• Markov Chain Monte Carlo

• Applications

By the end of the course participants will:

• Be able to understand the basics of Bayesian analysis

• Perform simple Bayesian analyses

• Apply simple models to their own work

 

Course format 

The course will be delivered over two days, with a two-hour live session on each day  (26/03/2025 and 02/04/2025 at 2pm).  Participants will be given access to a two-hour pre-recorded lecture one week prior to each live session, which they are expected to work through in preparation.  The live sessions will provide an opportunity to apply concepts from the lectures, and participants are encouraged to come with questions.

 

Pre-requisites  and preparatory materials

This course will be taught using STATA - Some basic knowledge of STATA will be required. 

The course leader will prepare the basics of probability refresher which can be used as part of the preparatory work for this course. It will be available 2 weeks before the course start date.  Students who are not as confident with using probability or haven’t worked with it recently are encouraged to complete this. 

 

Recommended Reading: 

STATA BAYESIAN ANALYSIS, REFERENCE MANUAL, RELEASE 18

Available here: https://www.stata.com/manuals/bayes.pdf [stata.com]

Bayesian Statistics for Beginners: a step-by-step approach, Therese Donovan,  Ruth M. Mickey

McElreath, Statistical Rethinking. 

Cost:

The fee for this course is:

£35 for students
£75 for staff working for academic institutions, Research Councils and other recognised research institutions, registered charity organisations and the public sector
£250 for all other participants.
In the event of cancellation by the delegate a full refund of the course fee is available up to two weeks prior to the course. NO refunds are available after this date.
If it is no longer possible to run a course due to circumstances beyond its control, NCRM reserves the right to cancel the course at its sole discretion at any time prior to the event. In this event every effort will be made to reschedule the course. If this is not possible or the new date is inconvenient a full refund of the course fee will be given.
NCRM shall not be liable for any costs, losses or expenses that may be incurred as a result of its cancellation of a course, including but not limited to any travel or accommodation costs. The University of Southampton’s Online Store T&Cs also continue to apply.

Website and registration:

Register for this course

Region:

North West

Keywords:

Quantitative Data Handling and Data Analysis, Bayesian methods, Markov Chain Monte Carlo (MCMC), Bayes’ Theoreom, Probability

Related publications and presentations from our eprints archive:

Quantitative Data Handling and Data Analysis
Bayesian methods
Markov Chain Monte Carlo (MCMC)

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