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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:

151–175 из 232 отзывов о курсе Байесовская статистика

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: 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: Yang X

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: Zhao L

4 авг. 2016 г.

This course covers a good amount of bayesian statistics. However, the presentation/videos starting from week 2 really sucks. They change instructors for difference topics and obviously some instructors are not very good at explaining other than reading the material.

The videos skipped many medium steps that are actually very crucial for understanding the concepts. And no suggested reading materials at all either. Also the quiz are not very well designed either. For example, some quiz are much more simpler than the course material, which makes it not helpful at all to understand the course material itself. While some times it is the opposite.

The first three courses in this specialization are very good, but somehow this course are way below the quality of the previous ones.

автор: Witold E W

26 сент. 2017 г.

Tons of interesting material. However, presented in a way which is hard to take, and harder to remember, especially if you are used to the exceptionally high standards of Coursera. The slides, which I am used to work with, are a big let down. They are hard to follow, erratic, lack thoroughness and are incomplete. It does not make it better that they refer you all the time to additional material. Also the lectures are disappointing. The lecturers do not interact with the slides, they don't explain. I wished I could have taken more from the course since I think that the topic is relevant and interesting. Really disappointed. I do hope that there will more MOOC's teaching Bayesian statistics soon.

автор: Camilo M

10 янв. 2021 г.

I think the course was for something more extended and, therefore, more understandable. A lot of reading material (which is appreciated) prior to the videos take a long time to start learning. I had hoped that by doing the laboratory of Week 3, I could go deeper into the concepts and understand many of the things that were more complex to assimilate, but the impossibility of executing certain functions and thus delay the test of the laboratory, was frustrating; this limits my continuity with week 4 and does not give me certainty that week 4 and 5, in the laboratory of R, is well designed and without problems. I think it has a lot of potential and opportunities for improvement.

автор: Jorge A S

10 июня 2018 г.

The previous courses of the specialization were much better. This one is too fast paced and confusing. The math for this course is significantly harder than for the previous, but in my case it was not the math what was making it hard. The videos are hard to follow. I answered some of the quiz questions based on intuition and what looked reasonable rather than actually knowing how to solve them. Usually in the previous courses the project felt like the hardest part, but on this one the project felt like the easiest. What I did like about the course is that it has good breadth of topics in Bayesian statistics.