Link to Content:

Bayesian Statistics

Created/Published/Taught by:

Coursera

Duke University

Mine Çetinkaya-Rundel

David Banks

Colin Rundel

Merlise A Clyde

Content Found Via:

Coursera

Free? Partially: Some Free Content, Some Paid

Cost Range:

$0.00 - $88.00

Tags: bayes theorem / Bayesian inference / bayesian models / R / RStudio / statistical inference / statistics

Difficulty Rating:

This is course 4 of 5 in the Statistics with R Specialization.

From Coursera:

“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.”

Recommended Prerequisites: "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.'"

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