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Kursteilnehmer-Bewertung und -Feedback für Bayessche Statistik von Duke University

756 Bewertungen
245 Bewertungen

Über den Kurs

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."...


20. Sep. 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.

9. Apr. 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 von 237 Bewertungen für Bayessche Statistik

von Zining J

9. Mai 2018

This is course is very intuitive!

von Subrata B

8. Sep. 2016

Excellent introductory course!

von Arman A

25. Aug. 2017

Very entertaining course !!!


25. Juli 2016

Very Nice Course! Excellent!

von Alexey K

10. Mai 2018

The magic course...)


von Harish

21. Juni 2018

Of an elvated level!

von Huynh T

24. Sep. 2019

It's helpful to me.

von Long K

16. März 2018

Strongly recommend!

von Donal G

7. Jan. 2017

Very good course.

von 李俊宏

22. Mai 2017

very intuitive!

von Tian Z

14. Dez. 2017

Pretty helpful

von John A

2. Okt. 2019

Great course!

von Pedro M

20. Dez. 2018

great course!

von Can Z

10. Jan. 2018

Great course.

von Denise L

2. Aug. 2018


von Oscar C R

28. Aug. 2020

Good Course

von Marina Z

27. Juni 2017


von hyunwoo j

16. Juli 2016

very useful

von Riku L

23. Dez. 2017












14. Juni 2020


von Byeong-eok K

31. Juli 2017


von Sanan I

4. Juni 2020


von Whitchurch R

26. Mai 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.

von Maurits v d M

22. Aug. 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.

von Pouya Z

26. Sep. 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.