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.

筛选依据：

创建者 Mark F C

•Jun 21, 2018

It was a good course, though I would include more coursework and exercises in R to assist with comprehending a difficult subject. Overall, good course for something that's difficult to teach.

创建者 Kian B

•Jul 29, 2016

The section about Beta-Binomial Conjugate is taught very fast and unless the student is quite familiar with Beta and Gamma distributions, it makes it very difficult to follow the course.

创建者 Jae S P

•Jul 18, 2017

This is one of many good courses that one can get a glimpse of Bayesian statistics though it lacks of thorough explanation of mathematical background and reading materials of any kind.

创建者 José L E N

•Jan 03, 2017

Theis course is substantially more difficult than the three first ones, and the material is scarce. However, I must admit that this is one of the courses I have ever learnt the most

创建者 Stanley R C

•Jan 30, 2018

The instructors have great expertise, but this course is pretty difficult for a Bayesian newbie. Additional study guides would be helpful (especially week 4).

创建者 Lalu P L

•Jun 02, 2019

The course could have been more comprehensive and less verbose. It had so much content in a tiny course. Content should be less and more comprehensive.

创建者 Malolan S

•Sep 10, 2019

A bit more depth in explaining conjugacy in priors and posteriors will be very helpful. A possible way would be to have more example illustrations.

创建者 Ángela D C

•Jun 12, 2018

Week 3 was too much information too soon, but week 4 was great again like the other courses in this specialisation. Learned so much, thanks!

创建者 KALYESUBULA M

•Jun 03, 2017

Learnt a lot. Though the subject material was hard to grasp first hand, it is good that instructor was readily available to help us through.

创建者 Adam A

•Aug 25, 2017

An interesting and challenging course, would be better with more real examples and explanation as some of the material felt rushed

创建者 Marwa A E K M A Z

•Jan 07, 2020

It's a good one, but not as previous courses. Week 3 isn't well explained as other weeks. Hope it can be further improved

创建者 Hanyu Z

•Dec 08, 2016

The material is good. However, there is no support from the instructors to answer our questions in the discussion forum.

创建者 Niels R

•Jul 06, 2019

This course through the material too fast. The content should have been spread out over two courses in my opinion.

创建者 Emmanouil K

•Aug 16, 2017

This is a very interesting topic. Lectures in weeks 3 and 4 could use some work.

创建者 Vicken A

•Dec 29, 2016

Bayesian stats is a broad topic. Learners would benefit from more material.

创建者 Raja F Z

•May 23, 2020

this Course very informative and bears an applied approach for learning.

创建者 Jaime R

•Nov 08, 2018

Theorethical backdrop is a bit excessive on an R focused course

创建者 Liew H P

•Jan 17, 2019

This course is challenging and well-presented!

创建者 José M C

•Mar 22, 2017

Good content but sometimes it gets confusing.

创建者 陈昊

•Nov 15, 2017

Harder than former courses but great!

创建者 George G R

•May 06, 2017

The classes are good.

创建者 sohini m

•Oct 27, 2017

It was nice

创建者 Haixu L

•Jan 19, 2018

The material is interesting. However some of the points are not presented in a way that I can understand.

The course is less coherent than the previous ones.

This course gave me an impression that the materials are not well organized. Basically, the course organizers present a lot of concepts and materials to you without background introductions. I know there are a lot to cover in 5 weeks. The organizers should think this through about how to present a lot of information in a short period of time. Maybe put the less important information in a lecture notes or something could be better.

创建者 Sander t C

•Jun 22, 2020

This course was way harder than the three that came before. It feels as if courses 1 to 3 did not prepare me for this one at all. The lecturers throw in a lot of formulas that they just expect us to understand with ease. Whereas the first three courses explained everything in great detail, even the simplest things, this course assumes you immediately understand everything they throw at you. The quizzes also ask for small details mentioned during 2 seconds of one of the many videos. Still, the course is doable if you push through and apply what you learn in the Rstudio-assignments.

创建者 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.