Back to Bayesian Statistics

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756 ratings

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245 reviews

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."...

RR

Sep 20, 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.

GH

Apr 9, 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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By 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.

By Camilo M

•Jan 10, 2021

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.

By 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.

By Juan R

•May 30, 2021

This course is way below the quality of the three previous ones. I is really a shame. I suggest it to be totally redesigned. The text book is not very well written. Besides grammatical errors it also has inconsistencies with what is taught in the lectures. As for the lectures they seem to have been done in a hurry, and many important aspects are not clearly explained. Coursera and Duke should think of splitting this course in two and leave the rush and lack of detail, that can be seen in this one, behind. I really believe this course is below Duke's standards and is a black spot in the Specialization.

By Natalie R

•Sep 5, 2019

This course, compared to the others in the specialization, was a bit of a mess. The lectures were hard to follow with fewer exercises to check your learning than in previous courses. The "text" seemed to just be a bad transcript of the lectures with all sorts of errors. The labs were confusing and sometimes included incorrect or outdated instructions that caused me to waste a lot of extra time trying to figure out what was wrong. I enjoyed doing the final project, though, and learned a lot doing that.

By Adara

•Dec 4, 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.

By 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.

By Sarthak R

•Dec 4, 2019

This course is far different from others in the series. Mathematical formulas and other concepts are introduced without any prior background. Even if the concept is understood the application part of it still remains a mystery on where to apply it, the course could have been more elaborate explaining these concepts in-depth rather than introducing without any prior background. Words such as prior families are used without introducing them properly.

By Matt H

•Aug 26, 2019

Disappointing drop in quality compared to previous courses in the specialisation. Lectures are just a verbatim copy of the accompanying book, with no additional context, and course assignments/quizzes expect you to know material not covered in the course (e.g. while working on a quiz, I would go back to the textbook, CTRL+F on key terms from the quiz questions, only for them not to be anywhere in the course material).

By Gustavo L

•Apr 26, 2020

This course was by far the hardest one of the series and I felt lost numerous times. The video lectures are brief and in my opinion bring more questions than answers. I am not sure about other students but I feel that this course needed 1- much more R-exercises. 2- many more examples per lecture for example, it could be better explored the lessons learned with multiple question quizzes.

By 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

By Lucie L

•Aug 15, 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.

By Xiaoping L

•Nov 2, 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.

By mausci71

•Aug 11, 2020

Bayesian statistics is hard, I get it. This is another reason not to throw a huge amount of concepts on students, with no explanations, nor any sense. I had to study Bayesian Statistics by myself, and out of this course. Please correct this issue.

By Omar S

•Mar 27, 2020

The instructors are not interactive at all, they are reading directly, it's very boring specially for first week, the instructor overlook most important issues and doesn't highlight them, however the reading material is useful.

By Léa E C B

•Mar 17, 2021

Way too hard compared to the others courses, and very unclear. Plus since not a lot of people finish the course, you have to wait a long time to see your peer review exam approved.

By 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

By 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.

By 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.

By Haochen Z

•Aug 25, 2020

After Week 2, there are large gaps between previous material and the futher teaching material which makes confusing and a bit hard to comprehend.

By amin

•Feb 25, 2021

I was following the courses very well until I got to this. I had trouble understanding almost all parts of this one:/

By Matti H

•Jan 15, 2017

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

By Wei C C

•Dec 6, 2018

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

By Jinru

•Dec 3, 2017

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

By sandhya r

•Sep 28, 2017

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

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