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Learner Reviews & Feedback for Bayesian Statistics by Duke University

753 ratings
243 reviews

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

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.

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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76 - 100 of 236 Reviews for Bayesian Statistics

By Zining J

May 9, 2018

This is course is very intuitive!

By Subrata B

Sep 8, 2016

Excellent introductory course!

By Arman A

Aug 25, 2017

Very entertaining course !!!


Jul 25, 2016

Very Nice Course! Excellent!

By Alexey K

May 10, 2018

The magic course...)


By Harish

Jun 21, 2018

Of an elvated level!

By Huynh T

Sep 24, 2019

It's helpful to me.

By Long K

Mar 16, 2018

Strongly recommend!

By Donal G

Jan 7, 2017

Very good course.

By 李俊宏

May 22, 2017

very intuitive!

By Tian Z

Dec 14, 2017

Pretty helpful

By John A

Oct 2, 2019

Great course!

By Pedro M

Dec 20, 2018

great course!

By Can Z

Jan 10, 2018

Great course.

By Denise L

Aug 2, 2018


By Oscar C R

Aug 28, 2020

Good Course

By Marina Z

Jun 27, 2017


By hyunwoo j

Jul 16, 2016

very useful

By Riku L

Dec 23, 2017












Jun 14, 2020


By Byeong-eok K

Jul 31, 2017


By Sanan I

Jun 4, 2020


By Whitchurch R

May 26, 2020

This is a good course. However, inorder to understand what the Professors are saying. I had to take a prelim course, to learn the vocabulary , as well as basic baysean concepts before attempting this course again. The course needs a certain level of accepting concepts in an abstract sense, and not being detail oriented while listening to the lectures, to gain understanding of the content. Also one needs to watch the videos again and again at a reduced speed to grasp what the professors say. This is certainly not an easy course, but the rewards are worth it. Once the student crosses a threshold of knowledge barrier. All in all this course has good content, without getting too caught up in the Math. I have not found better courses than this for Baysean Statistics.

By Maurits v d M

Aug 22, 2016

I had a lot of fun during this course, but I think it is simply too short to present all the topics in sufficient detail. Furthermore, I took this course without doing the prior courses in the specialization, and there were a couple of moments when I really thought previous knowledge from a different course was required.

I think for the most part the lecturers did a great job in explaining the materials in the course. The lectures themselves were also well structured, and the topics followed each other in a logical order. I would have loved to spend more time on modeling techniques and Markov Chain Monte Carlo.

By Pouya Z

Sep 26, 2019

The course was great and really informative. Particularly, it was interesting to get to work with BAS and statr packages that were developed, essentially, by the instructors. I, however, think that from decision loss functions onward, the course suddenly became way more complex. The normal conjugate families were not discussed on the previous lab, and I believe deserve to be emphasized with an example before heading to regression and reference priors. However, the notes were quite helpful. All and all, it was a great course.