Introduction to Bayesian Statistics

Date:

11/03/2025 - 12/03/2025

Organised by:

Royal Statistical Society

Presenter:

Richard Morey

Level:

Intermediate (some prior knowledge)

Contact:

training@rss.org.uk

Map:

View in Google Maps  (EC1Y 8LX)

Venue:

Online

Description:

This virtual course aims to provide a working knowledge of Bayesian statistics for interested researchers. 

Bayesian statistics has become a standard approach for many applied statisticians across a wide variety of fields due to its conceptual unity, clarity and practical benefits. However, because training in Bayesian methods is often not a standard part of research curricula, the benefits of Bayesian statistics have been slower to reach applied researchers.

Learning Outcomes

  • Understand the main differences and similarities between Bayesian and classical analysis
  • Understand basic concepts in Bayesian analysis, such as priors and posteriors
  • Formulate basic priors using knowledge from their area of expertise
  • Interpret the results of a Bayesian analysis
  • Use R and JAGS to perform a Bayesian analysis
  • Diagnose basic problems that can arise in Bayesian analysis

Topics Covered

  • Basic inference
  • Bayesian statistics
  • Markov Chain Monte Carlo
  • Multilevel models 

Target Audience

The target audience for this short course is researchers with a working knowledge of classical statistics who are curious about Bayesian statistics and how it can improve their statistical practice, and who want enough practical knowledge to start using Bayesian statistics. 

Knowledge Assumed

Basic knowledge of probability and common statistical techniques (t-tests, linear models, etc.). Basic working knowledge of R.

Cost:

Prices from £687.60 to £954 .00 (inc. VAT)

Website and registration:

Register for this course

Region:

Greater London

Keywords:

Quantitative Data Handling and Data Analysis, Bayesian Stats,. Markov Chain Monte Carlo


Related publications and presentations from our eprints archive:

Quantitative Data Handling and Data Analysis

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