Вернуться к Байесовская статистика

# Отзывы учащихся о курсе Байесовская статистика от партнера Университет Дьюка

3.8
звезд
Оценки: 767
Рецензии: 248

## 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:

## 1–25 из 241 отзывов о курсе Байесовская статистика

автор: Richard M

24 янв. 2019 г.

While the other modules so far have been terrific with good levels of support and clear explanations, this module is pretty terrible for a few reasons.

1) The level of support.

Your chances of getting a response to any question are slim - which means you're pretty much on your own here. Don't understand anything? Go find the answer elsewhere.

2) The tutors.

Mine Çetinkaya-Rundel has generally been terrific so far. Speaks slowly, repeats what variours terms mean (instead of assuming we memorize them the moment we hear them) and provides good clear examples to work from.

Sadly both Merlise and David are the opposite. They whiz through the material uncomfortably reading from a telepromter often assuming we instantly grasp every possible concept. It's almost impossible to follow most of the sessions they present. Most of the time there aren't even any exercises or opportunities to check we've understood the material correctly. They would both be 100% better if they frequently reminded us of the definitions of the concepts they use.

3) The material. There is FAR too much here to be covered in a single module. This is an entire course on its own (or a much bigger module).

4) Assumptions we know things which are never taught. I've lost track the number of times a word or concept sneaks into a quiz, into a lecture, or into an R package without explaining what it means. At times it feels this material was pulled from 2 or more sources and this has created gaps in understanding.

Sorry guys, I've really enjoyed the first three modules...but this one was a bit of a disaster.

Provide better support, shrink the material, create a better lecture experience and I'll happily revise this.

автор: Tansel T A

5 дек. 2018 г.

Unfortunately, for me, this course did not live up to my expectations in comparison to the previous 3 courses I took as part of the Statistics with R specialisation. I gave the previous 3 a full 5 stars each.

The problems I had with this course was not that my statistics knowledge was lacking or that I found it difficult. The problems were due to the robotic delivery of the material. Specially towards the end of the course. It is very clear that the instructors have a great depth of knowledge which is incompatible with the robotic delivery structure currently in place.

For example, if you use a particular technique, even if it was introduced earlier, all it takes is a brief 2-10 second statement to re-iterate. This encourages the delivery of the material to be a hybrid of both written text the instructors are reading from, as well as a more informal aura of discussion. A guideline is: 'Can you get someone off the street to read the material you wrote to the screen?'. The more this statement is false, the more amazing your course is.

Another issue I had was that the accompanying material was immense. Am I paying a subscription to read books and passages in order to understand the material? This point is also prevalent in the forums where it was raised multiple times. These books and supplementary material would be largely not required if simple commentary was in place in the videos.

E.g. We are applying a 'BIC' prior. What does this mean? Up to this point we are used to applying priors that are distributions. This means that we are approximating the posterior probabilities of the models using the maximisation of their log-likelihoods which turns out to be easier to calculate than the posterior distributions. However, as the model space grows (>25 parameters), we may need to rely on a sampling technique, these techniques which rely on posterior probabilities to traverse the model space. If I were to say this, it would take me 10 seconds but would provide so much information to the learner.

In summary, I could have read a lot of the presented material here from a text book and found it clearer which wasn't the case in the previous 3 courses. The resources were helpful and focused on interesting points. I loved the interviews at the end with experts in the field. The statsR package is great and this is a great way to showcase its capabilities. This course is OK but I think the delivery could be improved upon.

Thanks

автор: Sara M

23 дек. 2018 г.

Starts out good in the first week and then ramps up to graduate level statistics without really a lot of notation explanation. Week 3 with the silver haired lady as the teacher was the WORST. nothing made sense when she taught.

автор: Ong Y R T

16 мар. 2019 г.

Worst course in the specialization. Totally killed my interest in statistics and R. Warning to everyone, do not do this course if you have / want to learn statistics. Only do it if you want to re-enforce the view that statistics is not something for you.

автор: Anna P

24 окт. 2018 г.

Very large drop in quality from the previous three courses in the specialization. Unlike the previous classes, there is not a quality textbook provided. What passes for a "textbook" is essentially a written re-hashing of the lectures which provides no new examples. The lectures themselves can be hard to follow and often times skip over important calculations. There are no practice problems. The labs are also less clear and there are major leaps between what is taught and what we are expected to know to solve the problems. The lack of quality resources and poor teaching coupled with the more challenging material made this course very frustrating.

автор: Toan L T

26 янв. 2019 г.

Good for reviewing Bayesian Statistic. But not for new learners.

The quality is below the previous courses in the same Specialization. The contents are rushed. The labs are impractical and sometimes confusing.

And beware of the final assignment. Since the number of students is low, the grading takes lots of days. And you might miss the enrollment window for the Capstone course.

автор: Tulio C

11 дек. 2018 г.

The last two weeks are way too hard to follow and could provide more practical examples instead of focusing on mathematical theory and formulas. That would make more sense to this course when compared to the content of the previous ones in this specialization.

автор: Jo T

16 июля 2020 г.

While the rest of the courses in the "Statistics with R specialization" from Duke University were good beginner courses, intended for the rest of us, this course was a huge disappointment.

The so-called textbook is a hurriedly written set of bad lecture notes, all written in R markdown and automatically converted to HTML. On a normal 1920x1080 monitor, some of the math formulas in the later chapters are not visible, you either need a wider monitor, or must know enough HTML/CSS to use chromes "inspect" to edit the css so you can change the margins of the main content to "overflow: visible;" to see all of it.

The textbook/lecture notes and the video lectures use exactly the same content, so if you didn't understand one of them, don't bother to look at the other.

Concepts are not introduced in any logical order, and many concepts are hardly explained at all, instead they just throw a wall of jargon at you, possibly expecting you to just google it.

The amount of jargon is staggering, instead of focusing on a few basic ideas, graduate level concepts are constantly thrown around without any explanation (and remember that the basics are never covered).

The whole idea behind the course is confused, they say they will teach you Bayesian statistics without requiring calculus, yet the lectures and textbook are full of integrals. But even if you know calculus, don't expect to understand the math. The formulas introduced are not rigorous, but vague simplifications where you must already understand the content in order to understand why they can get away with the sloppiness.

In the video lectures, nobody explains any formulas or algorithms in detail. Instead the formulas (or in some cases, just the name of the formula or algorithm) are displayed in the corner while the professor continues reading the teleprompter without even stopping to breathe in.

And there is no flow to it. Reading the textbook or listening to the video lectures is like meeting a giant wall of words. No headings. No theorems or important formulas or algorithms highlighted. No dissection of things that are hard to understand. No exercises to make sure you understand the content. Instead important theorems are hidden in long examples. It was just page after page of heavy jargon without any logical structure. I found myself constantly wondering if they were still talking about the same thing, or had introduced yet another concept they didn't bother to explain.

I completed this course because I wanted to complete the specialization. But if you are curious about Bayesian statistics, and want to learn it, look elsewhere. This is not the course you are looking for.

автор: kpb

25 февр. 2018 г.

Like many other reviewers, I was caught out by the dramatic shift in approach and presentation style at week 3 of this class. In one of the in-class forum threads on this topic I found a perhaps sarcastic recommendation from one of the mentors to take a different class (UC Santa Cruz) which I did in the middle of this one. I learned more from that one. The in-video demonstrations do not always explain the numbers that show up on the screen, and there is much less direct connection to using R than in the previous classes in the specialization. In the final programming projects, the Bayesian magic is hidden behind packages so you don't actually work directly with the computations...just a different function call. This was the least rewarding class of the specialization, and I won't bother to continue to the capstone project because of it.

автор: Juan P S

12 авг. 2018 г.

I have been doing the specialization from course 1, giving 5 stars to all of them. This course had the poorest explanations in the videos. I like the content, as it is challenging but many questions come to mind which are not thoroughly discussed in the video. For example, when you do the exercises you get topics that raises questions and there is no way to clear these doubts. Sometimes in the forums, but it shouldn't be the case.

автор: mark n

30 июля 2018 г.

much different than the other classes in the series. They tried to put to much into this short course and consequently its way too hard. They should drop if from the statistics specialization and produce on new and longer stand alone Bayesian class. dissappointed because I dont think I can finish this class and now I wont be able to finish the specialization.

автор: Chen N

11 апр. 2019 г.

Clearly, Professor Clyde doesn't know how to teach.

автор: Val

26 февр. 2018 г.

Rushed exposition of extremely complex ideas. The level of this course is not at all consistent with that of the previous courses in the series. There is really no excuse for this; it's possible to provide an intuitive understanding of the Bayesian approach that is on par with the other courses (e.g. along the lines of Stone's "Bayes Rule: A Tutorial Introduction". This course has damaged my opinion of Duke's online offerings and Coursera more generally.

автор: Bugra Y

1 июня 2020 г.

Although I truly liked the first three courses in this specialization, I cannot say the same for this one. Especially, Week 3 and Week 4 are very very WEIRD. Instructors throw you new concepts and terminologies in each slide non-stop. I guess they expect you to have a doctorate in statistics beforehand and to be here for brainstorming about Bayesian statistics. Certainly, this is the WORST course in the specialization. I have finished and got the certificate but I learned absolutely nothing. I don't think it is possible to gain any knowledge in this way. I know this as I already completed the first three courses. This one was definitely a WASTE OF TIME. I am sorry for the good efforts that Mine Çetinkaya has put for the first three courses. Other instructors ruin it in this one. You can view a lot of reviews like this below. I don't understand why they don't fix this course and why they leave it as it is.

автор: Anna D

23 мая 2017 г.

A bit of a letdown after I loved the first three courses. It's a useful and interesting course. However, what I loved about the previous courses was that they were self-contained and if sometimes challenging I always managed to do the quizzes with the knowlegde and practice from the lectures and labs. With this one, I literally had to guess my way through some of the quizzes which is both frustrating and not very educational.

автор: Dario B

17 июня 2019 г.

I would suggest that you split this course in three components, mirroring the frequentist courses of the same specialization: introduction, inference and regression. That would make the material much more digestible, because today, it feels quite compressed and many things are left unexplained (specially the last two weeks of the course, I spent as much time there as with the rest of the specialization altogether!).

Kudos to the overall Specialization; I am not going to complete the last course due logistics, but I truly enjoyed. Specially the first three courses given by Profr. Mine Çetinkaya Rundel, she is a true educator; mastering not only the subject (Statistics) but also the art of teaching it. Her efforts towards quality statistical education are highly appreciated, at least by myself. Please keep on your great work!

автор: Roland

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.

автор: Wesley E

1 нояб. 2016 г.

Very good introduction to Bayesian Statistics. Very interactive with Labs in Rmarkdown. Definitely requires thinking and a good math/analytic background is helpful.

автор: Franco G

16 дек. 2016 г.

This is my first course on bayesian statistics, I really like it, it was step by step, and helps to clarify lots of concepts of frequentist statistic.

автор: Jorge R R

14 мая 2020 г.

This is a great course but challenging. Some modules are not that clear, so it is important to have other resources to study. I recommend this book to assist you over the course: https://xcelab.net/rm/statistical-rethinking/.

Nonetheless, I am now a fan of Bayesian statistics!!

автор: Praveen A

13 окт. 2018 г.

The course has seen a lot of improvement with new study materials and videos. I'd say that this is now much better than what the course was previously.

автор: Elizabeth C S

5 авг. 2018 г.

I think it was way too much subject matter to fit into the 5 weeks. In previous courses, I could watch lectures and take notes in a reasonable amount of time. This course, 7-minute lectures would take me 45 minutes to listen and take notes. They just went way too fast through the material, even talking much faster. Splitting this into 2 sections would have been much better.

автор: Rofi S I

19 авг. 2018 г.

Weeks 1 and 2 were perfect. Weeks 3 and 4 felt rushed. The course suddenly became very formula heavy.

автор: Chin J L

7 июня 2018 г.

In my opinion this is the most difficult course in this specialization. There is a lot of new concepts to master and, although Bayesian reasoning is more natural, most of us has been conditioned to think as a frequentist, not as a Bayesian. However, the course offered a glimpse on how Bayesian approach can deal certain issues where frequentist approaches fail and that is the most important lesson one can take home from this course.

It would be very helpful if the teachers provide us an indication of a good book on Bayesian Statistic that is friendly to people that are not mathematically oriented (like myself).

автор: Chen Y

6 июля 2017 г.

It was nice learning all the distribution functions and Bayesian statistics. However, I have one suggestion: When going through equations, it's better to dive a little deeper into them, or at least go through a few steps of derivation, rather than just show them on the screen. For example, in 'Bayesian Regression' when introducing 'conjugate bivariant normal-gamma distribution, it was directly given three correlations on the screen: (1) alpha | sigma^2 ~ N(a0, sigma^2 S_alpha, (2) beta | sigma^2 ~ N(b0, sigma^2 S_beta), (3) 1/(sigma^2) ~ G(mu_0/2, mu_0 sigma^2/2. There are many terms in the equation. It would be more learner friendly if one can at least go through what term corresponding to what. Or if time is a constraint one can at least show some reasonable reference, so that learners can search for papers. I had to do quite an amount of googling to get through these things.