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学生对 杜克大学 提供的 贝叶斯统计 的评价和反馈

3.8
751 个评分
243 条评论

课程概述

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."...

热门审阅

RR
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.

筛选依据:

101 - 贝叶斯统计 的 125 个评论(共 235 个)

创建者 Bram M

Aug 25, 2020

Great course, with good examples of how Bayesian stats work. It is incredibly difficult to follow, however, and would benefit from a more elaborate discussion of methodology (e.g. how (conjugate) priors are chosen, how to report results, etc). It currently is one long list of formulae, formulae, and formulae

创建者 Uira d M

Jul 3, 2019

The course is well structured but the span of topics is large and the complexity great. Maybe an extended version with more explanations and demonstrations of the equations would be better for understanding the whole concept of bayesian statistics, specially inference.

创建者 De'Varus M

Feb 14, 2019

Though this section in the specialization is a little more difficult than the other sections. The supplemental material provided is helpful in navigating through the course. I will continue to read through this material to further my understanding of the material.

创建者 Francisco M

Nov 9, 2019

The course is very interesting, but the jump from the previous course is too large. From calculating probabilities by hand and understanding the odds involved, to integrate distributions is too abrupt and not explained in detail.

创建者 Janio A M

Aug 9, 2018

Its a tough course however, I will just suggest to focus more on the practical side of things after doing all the theory. I really enjoyed course 4 where the professor used "R" to compare different models using the crime dataset.

创建者 zhaokai

Aug 16, 2016

I hope we can have access to the slides, and this can save attendees a lot of time, because I think after we finished watching the video we can skip relevant slides when we come across problems in doing exercise.

创建者 Mark F C

Jun 21, 2018

It was a good course, though I would include more coursework and exercises in R to assist with comprehending a difficult subject. Overall, good course for something that's difficult to teach.

创建者 Kian B

Jul 29, 2016

The section about Beta-Binomial Conjugate is taught very fast and unless the student is quite familiar with Beta and Gamma distributions, it makes it very difficult to follow the course.

创建者 JeaSung P

Jul 18, 2017

This is one of many good courses that one can get a glimpse of Bayesian statistics though it lacks of thorough explanation of mathematical background and reading materials of any kind.

创建者 José L E N

Jan 3, 2017

Theis course is substantially more difficult than the three first ones, and the material is scarce. However, I must admit that this is one of the courses I have ever learnt the most

创建者 Stanley R C

Jan 29, 2018

The instructors have great expertise, but this course is pretty difficult for a Bayesian newbie. Additional study guides would be helpful (especially week 4).

创建者 Lalu P L

Jun 2, 2019

The course could have been more comprehensive and less verbose. It had so much content in a tiny course. Content should be less and more comprehensive.

创建者 Malolan S

Sep 10, 2019

A bit more depth in explaining conjugacy in priors and posteriors will be very helpful. A possible way would be to have more example illustrations.

创建者 Ángela D C

Jun 12, 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!

创建者 KALYESUBULA M

Jun 3, 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.

创建者 Adam A

Aug 25, 2017

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

创建者 Marwa A E K

Jan 7, 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

创建者 Hanyu Z

Dec 8, 2016

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

创建者 Niels R

Jul 6, 2019

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

创建者 Emmanouil K

Aug 16, 2017

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

创建者 Vicken A

Dec 28, 2016

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

创建者 Raja F Z

May 23, 2020

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

创建者 Jaime R

Nov 8, 2018

Theorethical backdrop is a bit excessive on an R focused course

创建者 Elham L

Aug 25, 2020

The material was interesting, yet required more time.

创建者 Liew H P

Jan 16, 2019

This course is challenging and well-presented!