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Отзывы учащихся о курсе Байесовская статистика от партнера Университет Дьюка

3.8
звезд
Оценки: 760
Рецензии: 246

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:

151–175 из 239 отзывов о курсе Байесовская статистика

автор: Guillermo U O G

12 мая 2019 г.

I really loved the previous courses because their reading material which was very good complimented by the video lectures, nevertheless, in this course, many of the video lectures was the repetition of the main book.

автор: Pedro E

15 мар. 2018 г.

Course is much harder to follow than previous courses. Due to change of instructors, the notation used wasn't always introduced before and is not explained. Feels rushed if you hadn't previous notions of the subject.

автор: Sophie G

25 июля 2018 г.

Really hard to follow and finish, especially compared to the other classes in this specialization.

The concepts might be more complex, but the way they're taught also adds to the difficulty, in my opinion.

автор: Marcus V C A

6 июля 2020 г.

I think the content is very good, as well as the online book and the supplementary material. But the videos for Weeks 3 and 4 could be better ... In my opinion, they should be longer and more explanatory.

автор: Amy W

19 апр. 2020 г.

Until the last two weeks, this course was very good. The lectures in the last couple of weeks contained lots of information and not very many examples. The third week, especially, was overwhelming.

автор: Shaurya J S

20 мар. 2018 г.

Not as good as other courses in this specialization. Most of the times the focus was to teach the method of performing a Bayesian Statistical process rather than teaching the actual concept.

автор: Ganesh H

17 авг. 2017 г.

I felt the course ramps up from the basics way too quickly. I didn't like the pacing in the course compared to other courses in the same specialization, although I did learn a lot.

автор: Luv S

3 мая 2018 г.

Explanations not simplified as compared to the other courses in the specialisation. Very difficult to comprehend. Instructor should take more time to explain the fundamentals.

автор: Jennifer g e

10 апр. 2021 г.

I learned a lot but i think the teachers should explain with more examples, the things they explain seem very abstract and i had to look for extra help.

автор: Santiago S

14 июля 2018 г.

Se trata de explicar términos matemáticamente complejos de una manera muy general y vaga dificultando el entendimiento y el aprendizaje del tema.

автор: Tasmeem J M

6 авг. 2020 г.

This course gave me a hard time. The lectures from week 3 and 4 seemed difficult, some more resources would be helpful.

автор: Stephanie A

18 мар. 2020 г.

Like in all courses of this specialization, the peer assignment was a real bottle-neck in the completion of the course.

автор: Pauline Z

22 авг. 2020 г.

This is certainly a good introduction. But it did not help me to be independent on bayesian statistics

автор: dumessi

7 сент. 2019 г.

The explaining for some bayesian methods are unclear, which make it harder for new learner to follow.

автор: Robert M M

27 сент. 2017 г.

Slides poor compared to 3 earlier modules and instructor not as engaging. However, the labs are good.

автор: Stefan H

16 мар. 2019 г.

Find it hard to follow the lectures. The labs and supplement material is good though.

автор: Kalle K

16 июня 2020 г.

A useful course, but very demanding. Many of the lectures are fast-paced.

автор: Gustavo S B

17 сент. 2017 г.

I would recommend to include more weeks; slow down and go deeper

автор: Li Z

15 авг. 2019 г.

Some contents are just too difficult to understand fully.

автор: Christopher C

12 февр. 2018 г.

Very heavy information very quickly otherwise - great

автор: Derrick Y

4 дек. 2016 г.

Good course, but need more details.

автор: Xinyi L

14 авг. 2017 г.

not very interested

автор: Kshitij T

4 янв. 2018 г.

tough course.

автор: Vivian Y Q

12 окт. 2017 г.

huge jump

автор: Thomas C

4 авг. 2021 г.

Unfortunately I found this course to be inferior to the Inferential Statistics course. Below are some comments which will hopefully help you to improve the course.

- The lectures more or less repeated the textbook verbatim. When learning, it's helpful to be exposed to different examples. The lectures did not help if I didn't understand something from the textbook.

- The textbook was filled with spelling and grammatical errors, which affected my understanding at times. Ditto for the supplementary materials (although I did appreciate their presence).

- The pop-up windows for the questions during the lectures were poorly formatted (at least using Chrome).

- I would recommend redesigning the course. Begin by clarifying the philosophical differences between Inferential and Bayesian statistics. E.g. discuss the likelihood principle, the conditionality principle, etc. I had to look this stuff up myself to understand the fundamental differences between the two approaches.

- An explicit guide of when to apply different priors would have been useful. E.g. I have this data, I have this belief about it, I want to get this type of answer, so which prior should I use?

- I think a deep-dive on a smaller amount of material would have been better. It would have been helpful to slowly and deliberately go through each step of Bayesian inference, rather than rushing through a larger number of examples.

- Similar to the point above, it would have been helpful if you did not wrap the R functions. Instead, show us the source code and explain what each line does.

- The amount of time spent on Bayesian regression was puzzling, as the results were noted to be numerically equivalent to frequentist regression. Why not teach us something new, and only highlight the difference in interpretation?

Thank you, and I hope you can improve this course in the future. I would not recommend it at this point, although I would recommend the frequentist course.