Bayesian Statistics

Bayesian Statistics

Credential
Department

Statistical Science

Intermediate

English

5 weeks

Variable

Overview

“Bayesian Statistics” is course 4 of 5 in the Statistics with R Coursera Specialization. 

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.

Instructors

Mine Çetinkaya-Rundel
Mine Çetinkaya-Rundel

Associate Professor of the Practice in the Department of Statistical Science

David Banks
David Banks

Professor of the Practice of Statistical Science

Colin Rundel
Colin Rundel

Assistant Professor of the Practice of Statistical Science

Merlise Clyde
Merlise Clyde

Professor of Statistical Science

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