Introduction to Bayesian Statistics
11 & 12 March 2025
Royal Statistical Society
Summary
- Certificate of Attendance - Free
- Tutor is available to students
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Overview
This two-day course aims to provide a working knowledge of Bayesian statistics for interested researchers. It will be held on the 11 & 12 March 2025
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.
Certificates
Certificate of Attendance
Digital certificate - Included
Description
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
Who is this course for?
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.
Requirements
Basic knowledge of probability and common statistical techniques (t-tests, linear models, etc.). Basic working knowledge of R.
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