Module Aims: This module introduces students to Bayesian statistical methods in biomedical settings and their advantages and challenges, and provides skills for designing, assessing and interpreting Bayesian analyses using standard Bayesian statistical software.
Module Learning Outcomes:
By the end of the module, students should be able to:
- Understand the principles of Bayesian statistics: learning from data and judgements through probability distributions on parameters in models.
- Design a range of Bayesian models for health science problems, including appropriate selection of prior distributions.
- Implement the models in standard software, and assess the performance of computational algorithms used for this.
- Summarise and accurately interpret the output of Bayesian analyses.
- Understand the assumptions being made in Bayesian models, and effectively appraise and compare them both qualitatively and quantitatively using standard methods.
Pre-requisites: Either Statistics for Health Data Science or Advanced Biostatistics for Epidemiology.
Teaching Strategy: Lectures and computer practicals.
Assessment: Assessed take-home practical involving analysis of dataset(s) in R and JAGS.
Module Length: 4 days