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3.9

Оценки: 600

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Рецензии: 185

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."...

Sep 21, 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.

Apr 10, 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.

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автор: Jeff M

•May 09, 2019

Overall I think there are better options available for learning bayesian statistics. The pacing and structure of the course both felt off to me, spending too much time on some things (conjugacy in particular) and breezing past many other things too quickly (particularly numerical methods). I also thought that it would have been more helpful to learn to perform many of the analyses from scratch so that they could be better understood, rather than relying so heavily on the accompanying statsR package.

автор: Guillermo U O G

•May 12, 2019

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: schlies

•May 31, 2019

It seems like this course contains good information, but there's a huge gap in the material as taught by some of the instructors. It seems like one of the instructors in particular assumes you're already familiar with material that's not covered in the rest of the course. These parts of the lectures rehearse math and code in a very formulaic way which conveys almost no intuition or understanding of the subject matter. However, the labs a pretty good.

автор: Li Z

•Aug 15, 2019

Some contents are just too difficult to understand fully.

автор: dumessi

•Sep 08, 2019

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

автор: Elvis S R

•Sep 10, 2019

I don't think the level of this course is in continuity with the previous three of the specialization. The first average of the classes are as usual, but then the topics become harder and equation-oriented. Less examples are developed and I wasn't able to learn everything as it happened in the previous courses.

автор: Wei C C

•Dec 06, 2018

The materials and response from the organization are unavailable for a while and never get an answer

автор: Witold W

•Sep 26, 2017

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: Adara

•Dec 04, 2017

The course presents interesting material but it is not easy to follow. It is a huge jump from the previous courses and requires far more hours to understand all the (math-heavy) material than the stated. The slides feel a bit chaotic and the language/sentences during the explanations could be much simpler. At times it feels that the instructors limit themselves to reading formulas one after another, making it hard to find a connection between them and how they are applied.

автор: Minas-Marios V

•Apr 26, 2017

Honestly, I find it very hard to recommend this course to anyone. First of all, let's note that the course covers quite more advanced topics than the previous 3 courses in the specialization, so some extra difficulty is to be expected. However, the main problem I encountered was with the video lectures themselves, particularly during the 3rd and 4th week of the course. The instructor does a very rushed job at explaining everything, constantly giving us tons of information and jargon that is not previously mentioned, and even the examples fail to give us insight at what we need to do and why.

Other than the lectures, no external material is given to help us decipher what the professors are saying, other than a few Wikipedia links. I'm not saying that each course should be accompanied by an e-book, but honestly, if I wanted to learn about Bayesian Statistics from Wikipedia I could have well skipped this class.

The main reason I'm giving 2 stars to the course instead of 1 is the Labs and the Quizzes. Even though they could use some polishing too, especially the final Lab, they are indeed very helpful and do a much better job at clarifying the concepts presented.

All in all, I feel that if you want to learn about Bayesian Statistics you should look for another course, and/or save your money and get yourselves a good textbook.

автор: Joseph K

•Jan 24, 2017

I would've saved a lot of time by knowing the R commands used in this course. It took so long to figure out things and I I didn't like the course because of that.

автор: Lucie L

•Aug 16, 2016

This course clearly has come ambition to cover important topics on bayesian statistics, however, probably due to time limit, the lecturers have to skim through the contents without further, sometimes necessary explanations. As a result, the lectures are difficult to follow.

автор: Duane S

•Apr 15, 2017

This course makes a valiant effort to provide as much coverage of Bayesian statistical methods as the prior three courses in the "Statistics in R" specialization do for Frequentist statistical methods, but the lack of supporting material (e.g. reading/text exercises directly paired with each lesson) really hampers this. The videos are quite informative, but if you don't catch on to the material based strictly on the videos, the weekly quizzes can be a bit frustrating.

автор: George L

•Nov 23, 2016

Very theoretical and unstructured

автор: WONG, K C J

•Dec 03, 2016

Too Fast. Video is too short and spend a lot of time in the summary.

автор: Thomas P

•Aug 18, 2016

Mismatch between assessment and course content. After not being able to pass the assessment, I've fallen behind on the course and I'm too busy to catch up.

автор: Sandhya R

•Sep 28, 2017

A bit complicated compared to the other courses as part of the specialization

автор: Matti H

•Jan 15, 2017

Good introduction to Bayesian concepts, but the course would benefit of some rethought of design of exercises.

автор: Zhao L

•Aug 04, 2016

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: Kateryna M

•Jul 15, 2017

I think that some of the lectures in this unit are not constructed as well and clear as in previous units. This makes it harder to learn. I needed way more time than it is specified in the course to process and understand the course material. However, in the previous units I did not experience such issues

автор: Jinru

•Dec 03, 2017

good stuff but extremely hard to follow, not engaging at all. lecturer reads off the slides.

автор: Jorge A S

•Jun 10, 2018

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.

автор: Xiaoping L

•Nov 02, 2016

The professors know what they are doing but not good at making the concepts plain to the students who don't have the strong background. Most of the times I would just ask myself why they did this and that but later they don't provide enough explanations.

автор: David O P

•May 13, 2017

Although the course is high quality, unless the other units, this one is way too difficult. The fact that it wasn't Mine who performed the whole course impacts significantly

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