Back to Bayesian Statistics

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760 ratings

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246 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 Byeong-eok K

•Jul 31, 2017

Good

By Sanan I

•Jun 4, 2020

.

By Whitchurch R

•May 26, 2020

This is a good course. However, inorder to understand what the Professors are saying. I had to take a prelim course, to learn the vocabulary , as well as basic baysean concepts before attempting this course again. The course needs a certain level of accepting concepts in an abstract sense, and not being detail oriented while listening to the lectures, to gain understanding of the content. Also one needs to watch the videos again and again at a reduced speed to grasp what the professors say. This is certainly not an easy course, but the rewards are worth it. Once the student crosses a threshold of knowledge barrier. All in all this course has good content, without getting too caught up in the Math. I have not found better courses than this for Baysean Statistics.

By Maurits v d M

•Aug 22, 2016

I had a lot of fun during this course, but I think it is simply too short to present all the topics in sufficient detail. Furthermore, I took this course without doing the prior courses in the specialization, and there were a couple of moments when I really thought previous knowledge from a different course was required.

I think for the most part the lecturers did a great job in explaining the materials in the course. The lectures themselves were also well structured, and the topics followed each other in a logical order. I would have loved to spend more time on modeling techniques and Markov Chain Monte Carlo.

By Pouya Z

•Sep 26, 2019

The course was great and really informative. Particularly, it was interesting to get to work with BAS and statr packages that were developed, essentially, by the instructors. I, however, think that from decision loss functions onward, the course suddenly became way more complex. The normal conjugate families were not discussed on the previous lab, and I believe deserve to be emphasized with an example before heading to regression and reference priors. However, the notes were quite helpful. All and all, it was a great course.

By Ravichandran V

•Aug 6, 2019

Its really hard for me to follow this specific course, its as if I am reading a summary of a novel rather than a novel, ideally this course should be broken into two courses and made into two five week courses. I may need to take additional courses or read some books to get a clear understanding.

In the previous three courses the open stats book helped a lot, however, the online content for this course is difficult to follow as well.

By Bram M

•Aug 25, 2020

Great course, with good examples of how Bayesian stats work. It is incredibly difficult to follow, however, and would benefit from a more elaborate discussion of methodology (e.g. how (conjugate) priors are chosen, how to report results, etc). It currently is one long list of formulae, formulae, and formulae

By Uira d M

•Jul 3, 2019

The course is well structured but the span of topics is large and the complexity great. Maybe an extended version with more explanations and demonstrations of the equations would be better for understanding the whole concept of bayesian statistics, specially inference.

By De'Varus M

•Feb 14, 2019

Though this section in the specialization is a little more difficult than the other sections. The supplemental material provided is helpful in navigating through the course. I will continue to read through this material to further my understanding of the material.

By Francisco M

•Nov 9, 2019

The course is very interesting, but the jump from the previous course is too large. From calculating probabilities by hand and understanding the odds involved, to integrate distributions is too abrupt and not explained in detail.

By Janio A M

•Aug 9, 2018

Its a tough course however, I will just suggest to focus more on the practical side of things after doing all the theory. I really enjoyed course 4 where the professor used "R" to compare different models using the crime dataset.

By zhaokai

•Aug 16, 2016

I hope we can have access to the slides, and this can save attendees a lot of time, because I think after we finished watching the video we can skip relevant slides when we come across problems in doing exercise.

By 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.

By 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.

By JeaSung 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.

By José L E N

•Jan 3, 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

By Stanley R C

•Jan 29, 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).

By Lalu P L

•Jun 2, 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.

By 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.

By 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!

By KALYESUBULA M

•Jun 3, 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.

By 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

By Marwa A E K

•Jan 7, 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

By Hanyu Z

•Dec 8, 2016

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

By Niels R

•Jul 6, 2019

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

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