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Learner Reviews & Feedback for Bayesian Statistics by Duke University
798 ratings
About the 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."
Top reviews
AA
Aug 24, 2017
An interesting and challenging course, would be better with more real examples and explanation as some of the material felt rushed
NR
Jul 5, 2019
This course through the material too fast. The content should have been spread out over two courses in my opinion.
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251 - 255 of 255 Reviews for Bayesian Statistics
By Meta G
•Sep 5, 2023
There is almost no one taking this course so getting the peer review can take forever
By Ashish C
•Aug 29, 2019
The quality of teaching was drastically down as compared to other courses.
By Jeffrey W
•Jun 2, 2018
Unclear information, too vague, incomplete presentation of ideas.
By Jose C G
•Dec 5, 2022
It is a pity that the course is for R
By Shubham J
•Sep 15, 2019
becomes too much confusing at times.