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Learner Reviews & Feedback for Bayesian Statistics by Duke University
791 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
MR
Sep 20, 2017
Great course. Difficult to apprehend sometimes as the Frequentist paradigm is learned first but once you get it, it is really amazing to see the believe update in action with data.
GH
Apr 9, 2018
I like this course a lot. Explanations are clear and much of the (unnecessarily heavyweight) maths is glossed over. I particularly liked the sections on Bayesian model selection.
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251 - 253 of 253 Reviews for Bayesian Statistics
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.