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 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.

GH

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.

필터링 기준:

교육 기관: 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).

교육 기관: 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.

교육 기관: 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

교육 기관: 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.

교육 기관: 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.

교육 기관: Maurizio S

•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.

교육 기관: 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.

교육 기관: 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

교육 기관: 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.

교육 기관: 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.

교육 기관: Haochen Z

•Aug 26, 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.

교육 기관: Matti H

•Jan 15, 2017

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

교육 기관: Wei C C

•Dec 06, 2018

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

교육 기관: Jinru

•Dec 03, 2017

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

교육 기관: sandhya r

•Sep 28, 2017

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

교육 기관: CHIDI O

•Aug 04, 2019

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

교육 기관: KA C W

•Dec 03, 2016

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

교육 기관: Juhong P

•Oct 03, 2019

Too difficult to catch up each week.

교육 기관: George L

•Nov 23, 2016

Very theoretical and unstructured

교육 기관: 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.

교육 기관: 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.

교육 기관: 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.

교육 기관: 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.

교육 기관: 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.