Level: Intermediate (I)

This two-day 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
• 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.

Richard Morey

Richard D. Morey is a senior lecturer in the School of Psychology at Cardiff University where he specializes in research in the theory and practice of statistical methodology. He obtained his PhD in cognition and neuroscience and a Masters degree in statistics from the University of Missouri.

Fees		Registration before  16 February 2021	Registration on/after 16 February 2021
Non Member  RSS Fellow  RSS CStat/GradStat/Data Analyst  also MIS & FIS		£611.00+vat  £520.00+vat  £490.00+vat	£680.00+vat  £577.00+vat  £543.00+vat