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Avis et commentaires pour d'étudiants pour Statistiques bayésiennes par Université Duke

786 évaluations

À propos du cours

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."...

Meilleurs avis


20 sept. 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 avr. 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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126 - 150 sur 249 Avis pour Statistiques bayésiennes

par Ángela D C

12 juin 2018

Week 3 was too much information too soon, but week 4 was great again like the other courses in this specialisation. Learned so much, thanks!


3 juin 2017

Learnt a lot. Though the subject material was hard to grasp first hand, it is good that instructor was readily available to help us through.

par Adam A

25 août 2017

An interesting and challenging course, would be better with more real examples and explanation as some of the material felt rushed

par Marwa A E K

7 janv. 2020

It's a good one, but not as previous courses. Week 3 isn't well explained as other weeks. Hope it can be further improved

par Hanyu Z

8 déc. 2016

The material is good. However, there is no support from the instructors to answer our questions in the discussion forum.

par Niels R

6 juil. 2019

This course through the material too fast. The content should have been spread out over two courses in my opinion.

par Emmanouil K

16 août 2017

This is a very interesting topic. Lectures in weeks 3 and 4 could use some work.

par Vicken A

28 déc. 2016

Bayesian stats is a broad topic. Learners would benefit from more material.

par Raja F Z

23 mai 2020

this Course very informative and bears an applied approach for learning.

par Jaime R

8 nov. 2018

Theorethical backdrop is a bit excessive on an R focused course

par Elham L

25 août 2020

The material was interesting, yet required more time.

par Liew H P

16 janv. 2019

This course is challenging and well-presented!

par José M C

22 mars 2017

Good content but sometimes it gets confusing.

par 陈昊

14 nov. 2017

Harder than former courses but great!

par George G

6 mai 2017

The classes are good.

par sohini m

27 oct. 2017

It was nice

par Tanika M

8 sept. 2020

I don't have much new to add here - like many others, I found the course to be a sharp departure in teaching style and workload from the previous 3 courses, and found unanswered threads on the forums from one and two years ago. Students have been leaving feedback in this vein for years as well but it does not seem to have prompted any adjustments. The last two weeks of reading are especially intense and feel very crammed in, with the videos not explaining it with the care that the first few courses do.With all that said, it is not impossible to get through this course (clearly, as many of us have finished it), but you're left on your own for much of it. On the bright side the course project is not a huge jump up in difficulty as the readings may suggest, but is pretty much in line in terms of difficulty compared to the previous three projects.

par Haixu L

19 janv. 2018

The material is interesting. However some of the points are not presented in a way that I can understand.

The course is less coherent than the previous ones.

This course gave me an impression that the materials are not well organized. Basically, the course organizers present a lot of concepts and materials to you without background introductions. I know there are a lot to cover in 5 weeks. The organizers should think this through about how to present a lot of information in a short period of time. Maybe put the less important information in a lecture notes or something could be better.

par Sander t C

22 juin 2020

This course was way harder than the three that came before. It feels as if courses 1 to 3 did not prepare me for this one at all. The lecturers throw in a lot of formulas that they just expect us to understand with ease. Whereas the first three courses explained everything in great detail, even the simplest things, this course assumes you immediately understand everything they throw at you. The quizzes also ask for small details mentioned during 2 seconds of one of the many videos. Still, the course is doable if you push through and apply what you learn in the Rstudio-assignments.

par Sandro H

29 nov. 2020

I wanted to rate this 3.5 but because I could not will lean towards 3 stars. The main reasons being that the course could have been organized in such as way as to introduce Bayesian statistics, rather than bombard the student with hefty math formulas. Instead, it could have reduced the material by half and focused on understanding WHY and WHEN Bayesian can be helpful for certain cases. An emphasis on providing more practical examples could have helped me more clearly understand the mechanisms of priors, model averaging, etc.

par Jeff M

9 mai 2019

Overall I think there are better options available for learning bayesian statistics. The pacing and structure of the course both felt off to me, spending too much time on some things (conjugacy in particular) and breezing past many other things too quickly (particularly numerical methods). I also thought that it would have been more helpful to learn to perform many of the analyses from scratch so that they could be better understood, rather than relying so heavily on the accompanying statsR package.

par gabriel c p

15 févr. 2022

The classes in this course are less illustrative and spend less time in each topic in comparison with the Frequentist courses of the Statistics specialization. From my perspective, this block could be a whole specialization on its own. I really enjoyed learning about Bayesian statistics and I am thinking of taking a more detailed course only on this. Especially because this curse didn't help me understand Bayesian Statistics as well as it helped me understand Frequentist Statistics.

par Greg S

31 mai 2019

It seems like this course contains good information, but there's a huge gap in the material as taught by some of the instructors. It seems like one of the instructors in particular assumes you're already familiar with material that's not covered in the rest of the course. These parts of the lectures rehearse math and code in a very formulaic way which conveys almost no intuition or understanding of the subject matter. However, the labs a pretty good.

par Meng ( L

8 déc. 2017

This course is different from the first 3 courses in this specialization. I only recommend this course to people who have sound knowledge in calculus and some background knowledge in Bayesian Statistics. Personally, the pace of the videos is fast and the instructors use very technical terms. Although the course is not intended to give in-depth explanation into Baysian statistics, how the content is set up tend to be confusing.

par Abdallah N

23 juil. 2021

This was course #4 of the series. The previous 3 courses were fun. Not so for Bayesian stat. While working out the quizzes and project were at reach, the concept behind several aspects of bayesian remain obscure, at least for me. Instructors are wonderful, they know their stuff, but something was missing as to delivering bayesian stat. Not their fault: bayesian looks like it's a hard core topic to understand and master.