Share Description
In this course, you will learn:
- Some basic notions about random variables.
- Some basic features of probability distributions.
- Use of Bayes' rule in data analysis.
- The idea of likelihood and its application in Bayesian statistical modeling.
- Bayesian regression models with brms (a Stan front-end).
- How to see and comprehend the previous and posterior distributions.
- How to create previous and posterior prediction distributions while evaluating models.
- How to read the output of simple regression models.
Syllabus:
- Initial Setup
- Bayesian data analysis
- Computational Bayesian data analysis
- Bayesian regression and hierarchical models
