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Отзывы учащихся о курсе Байесовская статистика от партнера Университет Дьюка

3.8
звезд
Оценки: 741
Рецензии: 240

О курсе

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."...

Лучшие рецензии

RR
20 сент. 2017 г.

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
9 апр. 2018 г.

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.

Фильтр по:

101–125 из 232 отзывов о курсе Байесовская статистика

автор: Bram M

25 авг. 2020 г.

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

автор: Uira d M

3 июля 2019 г.

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.

автор: De'Varus M

14 февр. 2019 г.

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.

автор: Francisco M

9 нояб. 2019 г.

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.

автор: Janio A M

9 авг. 2018 г.

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.

автор: zhaokai

16 авг. 2016 г.

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.

автор: Mark F C

21 июня 2018 г.

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.

автор: Kian B

29 июля 2016 г.

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.

автор: Jae S P

18 июля 2017 г.

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.

автор: José L E N

3 янв. 2017 г.

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

автор: Stanley R C

29 янв. 2018 г.

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

автор: Lalu P L

2 июня 2019 г.

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.

автор: Malolan S

10 сент. 2019 г.

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.

автор: Ángela D C

12 июня 2018 г.

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!

автор: KALYESUBULA M

3 июня 2017 г.

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.

автор: Adam A

25 авг. 2017 г.

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

автор: Marwa A E K

7 янв. 2020 г.

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

автор: Hanyu Z

8 дек. 2016 г.

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

автор: Niels R

6 июля 2019 г.

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

автор: Emmanouil K

16 авг. 2017 г.

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

автор: Vicken A

28 дек. 2016 г.

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

автор: Raja F Z

23 мая 2020 г.

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

автор: Jaime R

8 нояб. 2018 г.

Theorethical backdrop is a bit excessive on an R focused course

автор: Elham L

25 авг. 2020 г.

The material was interesting, yet required more time.

автор: Liew H P

16 янв. 2019 г.

This course is challenging and well-presented!