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3.9

Sterne

625 Bewertungen

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191 Bewertungen

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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von Jinru

•Dec 03, 2017

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

von Sandhya R

•Sep 28, 2017

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

von Chidi O

•Aug 04, 2019

Poor lectures. Please look at the feedbacks on this given in the forums

von WONG, K C J

•Dec 03, 2016

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

von Juhong P

•Oct 03, 2019

Too difficult to catch up each week.

von George L

•Nov 23, 2016

Very theoretical and unstructured

von Markus S

•Sep 07, 2016

About two years ago I completed Dr. Mine's course "Data Analysis and Statistical Inference" and was quite impressed by it. I always hoped that there'd be a follow up on bayesian statistics, so I was really excited when I heard that a course on this topic had finally been created. However while attending the course I became more and more disappointed. Dr. Mine does a nice job explaining things, other teachers in this course aren't as talented. Most slides / videos are quite useless for teaching because they skip over important steps without giving appropriate explanations. Also I was quite disappointed that this course pretty much only focuses on conjugate priors. MCMC is only skimmed over and the introduction to MCMC is more than questionable - instead of showing a simple example, MCMC is squeezed into the topic of bayesian model selection. Another point is R - this course doesn't really teach bayesian stats with R. It teaches how to call one-liners like bayes_inference (from package statsr) or bas.lm (from package BAS) instead of lm. This is totally disappointing. I wish this course would skim over conjugate prior methods and then focus on MCMC sampling methods by teaching how to build interesting and practically useful models using JAGS/STAN/PyMC/whatever. For anyone interested in bayesian stats I'd recommend reading "Doing Bayesian Data Analysis - Using R, JAGS, and STAN" and "Probabilistic Programming and Bayesian Methods for Hackers". These books are actually cheaper than this course.

von Donald A C

•Apr 09, 2017

The first three courses in this Duke series were superbly well done. I have taken numerous courses from Harvard and Johns Hopkins, and none of them compare in quality of execution of the first three Duke courses in this series.

And then there was Bayesian Statistics: much of the "instruction" in this course was truly awful. The quality of the slides and video and so on was still excellent, but the "teaching" was horrible. Vast amounts of totally unexplained jargon and very extensive equations were thrown at the students with the apparent assumption that the course was a review for postdoctoral statistics students. When material is beyond the scope of what perspective students can reasonably be expected to understand, faculty members should be honest enough to just say so rather than pretending to teach the subject matter.

I appreciate very much what the Duke faculty achieved in the first three courses, but the treatment of Bayesian statistics that I have just suffered through was shameful.

von Lee E

•Nov 20, 2016

The first three classes in this certification were excellent; this course was anything but that. There seems to be a significant disconnect between the first three courses (probability, inference, linear regression) and the fourth course (bayesian). I do not have a strong statistics background but I felt the first three classes in the certification challenged me, while providing an adequate level of support and thorough / articulate examples; the pace was perfect. Yet, with the fourth course I believe that either: 1) there needs to be a bridge course that prepares you for the bayesian course, or 2) the material needs to be taught at a slower pace with more specific and well presented examples / frameworks to work from. Although I was able to complete the course, I will now have to find an alternative source to learn from in order to really understand bayesian stats.

von Aydar A

•Dec 20, 2017

The worst course in the series.

It progresses at a hurricane speed, thus as usefull as the Maria. I have barely made and it was not a pleasant experience. In fact I drowned at the week 4. The only reason I did not drop the course is because I've already paid for the previous courses of the specialization and I need to complete specialization for the certificate.

I think only people who had bayesian stats before and take this course as a refresher might find it pleasant. Or people with very good knowledge of probability theory. For others it is just a waste of time, because you will not learn to sail during a hurricane.

I have checked the syllabus of the other course on Bayesian Stats offered on coursera and it covers the same material in 8 weeks(2 courses), so that course would probably be a better choice if you are considering taking this course individually.

von John H

•Jan 28, 2020

The pace of this specialization increased rapidly with this course. It of course makes sense that as the specialization goes on, the coursework would become more challenging and require more time. However, this was such a leap from previous courses that I feel as if it should be in a different specialization. In every lesson, I felt inundated with complex calculations and formulas that were way above my head. I think that this course spend way too much time on theory (and breezing through it!) and not enough time on R. Why not walk us through multiple Bayesian examples in R? That would actually be helpful. As is, this is a course that I needed to sog through for the specialization. One star.

von Alois H

•May 21, 2017

After a brilliant start of the specialization with courses Introduction, Inference and Regression, the Bayesian course comes as a harsh disappointment.

Weeks 1 and 2 give a useful introduction to Bayes' rule. However, I haven't learnt anything of significance after that. The main instructor's explanations are unclear, and in almost every single video there's a point where there's just too much confusion to get the overall message. This is extremely frustrating and, as mentioned, in sharp contrast to the other courses.

In my opinion this course would urgently need to be re-recorded. Preferably, with a lot more input from Dr Cetinkaya-Rundel, who's an extremely gifted teacher.

von Chengyu H

•Jul 21, 2016

I don't understand how come this course can get such high reviews. My experience with this course is horrible. First of all, most quiz are poorly designed, lots of mistakes. For instance, there are 10 Qs in week 1, 3 of them have mistakes. Wasted me tons of times.

Lectures are also difficult to follow. Instructors usually just give formulas without further explanation. I forced myself to go through them until week 4, I finally give it up. I feel like it is a waste of my time. I need to find a better course on this topic.

Most coursera courses are very well designed. This one is the worst I have ever experienced.

von Erik F

•Jun 19, 2017

Unlike the previous sections in this specialization, this one has no reading material, nor does it have many problem sets to solve. You will definitely need to find external resources in order to complete this section, because numerous concepts are glossed over, explained vaguely, or explained poorly. I recommend Kruschke's "Doing Bayesian Data Analysis" as a very accessible way to learn Bayesian statistics. I'd have no confidence using Bayesian approaches in practice from only the material taught in this section. Frankly, this section seems like it was hastily thrown together, and I was very disappointed.

von Eszter A

•Sep 13, 2016

This course needs much more work from instructors before it gets offered to the public. It is poorly assembled, offers hardly comprehensible material with no or very few resources to turn to. Reading material is listed, but they are useful for people already skilled in Bayesian Statistics. Exercises are worded such, that even the questions are a challenge to understand. Quizzes contain material never mentioned during lessons. Discussion forums are left unanswered by the teaching staff - or if they reply, they do it in a very negligent manner. No support on the merits. A major disappointment.

von Graham G

•Oct 01, 2019

This course is awful, especially compared with the rest of the courses in the specialization. I had to read an entire Bayesian statistics text book in order to understand this area, and this courses still made little sense. This specialization is supposed to be for beginners and yet this course gets into intense mathematical notation with no preparation or guidance. I have somewhat of a math background, and this course was not only extremely difficult to finish, I don't feel like I really learned much of anything at the end. This course needs to be redesigned from the ground up.

von Marina C R

•Jul 31, 2017

Unlike the first 3 courses of this specialization, which were excellent, this one is not recommendable at all. As many other students have reported, the teaching material is not enough neither to understand the subject nor to do the graded material. I am really disappointed because the problem seems to come at least 4 months ago but the teacher (which by the way is far to be as good as Mine) has not replied. Instead, mentors have suggested to use the forums to make questions but it is neither affordable nor acceptable.

von Renat M

•Sep 08, 2017

The course is too sketchy: it does not provide enough materials to grasp the main ideas of Bayesian Statistics nor it gives any details about some formulas and important principles.

This course does not have a book to follow along as the previous courses had (statistics).

I had to spend more than 2 months to be able to understand all the concepts that this course was trying to teach. In this sense watching Youtube videos and reading papers was much more helpful than the entire course itself.

von Cindy C

•Feb 05, 2017

This class assumes a lot of statistical knowledge and background that is not covered in the first three classes of the series. So much statistical terminology and jargon was used by the instructor, it felt like taking a class in another language where I had to constantly stop the video and google for the terminology she used. It took a lot of grit to finish the class, which was overall a very demoralizing and negative experience.

von Ilya P

•Sep 13, 2017

While the first 3 courses had ample examples, guided practices, and other tools to learn, this course does not. Quizzes do not have good explanations, and videos do not have guided practice. There is no book to follow, hence, learning the material is difficult.

Instructors need to rework the course to include books, guided practices, and guided R examples in order to aid comprehension.

von Ben R

•Apr 08, 2018

A frustrating course, especially when compared to the other courses in this specialization. Lectures alternated between over my head and not giving enough information. Projects seemed designed for someone with a better grasp of R. I will probably look for another course on Bayesian statistics, because I feel my grasp of these concepts is still weak.

von Andrew O

•Aug 11, 2017

The change of instructors negatively affected this class. The new instructors are nowhere near as good at explaining the data and tending to start talking about things without even explaining what they where to to use a lot of activations, which one would need to continually look up.

von Naren T

•Dec 26, 2019

Very poor explanation in week 3, the new professor is not explaining the definitions or the use of them properly. Too many jargons.

Professor doesnt explain the use of prior predictive distribution and just introduces the formula without any consideration for explanation

von QIAN Y

•Jul 29, 2016

The course lacks of explanation and it's very difficult to follow. It seems that the instructor just reads the slides without reasoning and explanation. Suggested reading materials are needed.

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