About this Course
This course describes Bayesian statistics, in which one's inferences about parameters or hypotheses are updated as evidence accumulates. You will learn to use Bayes’ rule to transform prior probabilities into posterior probabilities, and be introduced to the underlying theory and perspective of the Bayesian paradigm. The course will apply Bayesian methods to several practical problems, to show end-to-end Bayesian analyses that move from framing the question to building models to eliciting prior probabilities to implementing in R (free statistical software) the final posterior distribution. Additionally, the course will introduce credible regions, Bayesian comparisons of means and proportions, Bayesian regression and inference using multiple models, and discussion of Bayesian prediction. We assume learners in this course have background knowledge equivalent to what is covered in the earlier three courses in this specialization: "Introduction to Probability and Data," "Inferential Statistics," and "Linear Regression and Modeling."
What You'll Learn
- Use Bayes’ rule to update probabilities
- Understand Bayesian theory and perspective
- Apply Bayesian methods to practical problems
- Build Bayesian statistical models
- Perform Bayesian prediction and inference
Prerequisites
- Basic knowledge of statistics and probability
- Readiness for applied work
Instructors
Mine Çetinkaya-Rundel
Associate Professor of the Practice
David Banks
Professor of the Practice
Colin Rundel
Assistant Professor of the Practice
Merlise A Clyde
Professor