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.

필터링 기준:

교육 기관: Jo T

•Jul 16, 2020

While the rest of the courses in the "Statistics with R specialization" from Duke University were good beginner courses, intended for the rest of us, this course was a huge disappointment.

The so-called textbook is a hurriedly written set of bad lecture notes, all written in R markdown and automatically converted to HTML. On a normal 1920x1080 monitor, some of the math formulas in the later chapters are not visible, you either need a wider monitor, or must know enough HTML/CSS to use chromes "inspect" to edit the css so you can change the margins of the main content to "overflow: visible;" to see all of it.

The textbook/lecture notes and the video lectures use exactly the same content, so if you didn't understand one of them, don't bother to look at the other.

Concepts are not introduced in any logical order, and many concepts are hardly explained at all, instead they just throw a wall of jargon at you, possibly expecting you to just google it.

The amount of jargon is staggering, instead of focusing on a few basic ideas, graduate level concepts are constantly thrown around without any explanation (and remember that the basics are never covered).

The whole idea behind the course is confused, they say they will teach you Bayesian statistics without requiring calculus, yet the lectures and textbook are full of integrals. But even if you know calculus, don't expect to understand the math. The formulas introduced are not rigorous, but vague simplifications where you must already understand the content in order to understand why they can get away with the sloppiness.

In the video lectures, nobody explains any formulas or algorithms in detail. Instead the formulas (or in some cases, just the name of the formula or algorithm) are displayed in the corner while the professor continues reading the teleprompter without even stopping to breathe in.

And there is no flow to it. Reading the textbook or listening to the video lectures is like meeting a giant wall of words. No headings. No theorems or important formulas or algorithms highlighted. No dissection of things that are hard to understand. No exercises to make sure you understand the content. Instead important theorems are hidden in long examples. It was just page after page of heavy jargon without any logical structure. I found myself constantly wondering if they were still talking about the same thing, or had introduced yet another concept they didn't bother to explain.

I completed this course because I wanted to complete the specialization. But if you are curious about Bayesian statistics, and want to learn it, look elsewhere. This is not the course you are looking for.

교육 기관: Bugra Y

•Jun 01, 2020

Although I truly liked the first three courses in this specialization, I cannot say the same for this one. Especially, Week 3 and Week 4 are very very WEIRD. Instructors throw you new concepts and terminologies in each slide non-stop. I guess they expect you to have a doctorate in statistics beforehand and to be here for brainstorming about Bayesian statistics. Certainly, this is the WORST course in the specialization. I have finished and got the certificate but I learned absolutely nothing. I don't think it is possible to gain any knowledge in this way. I know this as I already completed the first three courses. This one was definitely a WASTE OF TIME. I am sorry for the good efforts that Mine Çetinkaya has put for the first three courses. Other instructors ruin it in this one. You can view a lot of reviews like this below. I don't understand why they don't fix this course and why they leave it as it is.

교육 기관: Matthew C

•Jul 26, 2020

This course is absolute garbage. Halfway through week 3 nothing makes sense anymore. They switch up the teachers on you and start throwing massive formulas in your face with huge chunks of R code that only use functions made specifically for this course that act like black boxes where I have no idea what they're doing. The instructional content is all super old and doesn't even make sense. Their R package doesn't even work anymore, they have all kinds of grammatical errors and typos in the videos and written materials, and none of the lessons feel like they build on information from previous lessons.

I'm really interested in Bayesian statistics and have a strong stats background and this course has only made me more confused than before I started. I've never been so frustrated with a course (online or in person) in my entire life. Don't waste your time or money.

교육 기관: Shivang S

•Jun 18, 2020

Unlike the 3 prior courses in this specialization, Bayesian Statistics is not taught well at all. I had the following issues with the course: (1) The course book is full of typos making it frustrating to read (2) You are expected to understand lots of statistical jargon that is not introduced in prior courses or even explained well in the course that it is being used in (3) The lessons are not contextualized into how these techniques are applied to the real world.

It's disappointing to go through 3 courses that were taught extremely well by Professor Mine Çetinkaya-Rundel only to end it on a course like this which seemed to be half-baked and speedily covered. There may be other courses on Bayesian Statistics on Coursera that teach this increasingly important field better.

교육 기관: Alexander C

•Jul 16, 2020

Taking this course was honestly a really upsetting experience.

Starting in week 3 the instructor you are used to gets swapped out for someone who simply cannot communicate concepts or ideas. Your only options will be to either start gaming the quizzes or find external resources to figure out what is going on. Even in the latter case, the course expects you to use their own custom functions in R for the assignments. They no longer maintain the course or their R library and some of the function were incomplete to begin with. Anyway, even when you fight your way through it and then wait days for the peer reviews to come in, you will try to finally move on to the capstone project just to find it locked until some arbitrary start date set one to two months in the future.

교육 기관: Syed S R

•Sep 13, 2018

I want to give 0 ratings. The worst course I have seen so far in Coursera. Horrible planning, horrible execution and makes no sense. Totally disappointed by the style of course design and delivery

교육 기관: vacous

•Jun 06, 2017

The professors tried to put too much material into very short videos.

The result is that most of the material are explained unclearly.

교육 기관: Daniel R

•Jun 14, 2017

they didn't tell that this course didn't have a homework, reading, or practice problems to do. Ended up s

교육 기관: Minasian V

•Aug 16, 2016

This course was the most challenging one among all courses in specialization. I wish there were more explonation of how we get smth from smth and not like " and it appears to be equal .." and so on.

I also had to watch Ben Lambert's bayesian course on YouTube to understand the material of the second week,because Prof. David Banks was not good enough in explanation.

Assistant Prof. Mine Çetinkaya-Rundel has an amazing teaching skills.

Prof. Merise Clyde is good in explonations and I understand that she tried to present a very complicated material in a simple way, but as I have already mentioned above, I wish there were more explonations of casuality of the formulas with examples and Intuition that stands behind these formulas like in Ben Lambert's videos.

Many thanks for such an amazing experience.

교육 기관: Andrea P

•Nov 12, 2016

Very interesting and formative. It starts from the basics (Bayes' theorem) but then it goes beyond the usual conjugate models such as Beta-binomial and Gamma-Poisson. Bayesian Linear Regression, Bayes Factors, Bayesian Model Averaging and a brief introduction to MCMC are provided. This really put me in the position of applying Bayesian Statistics to some real world application: the final test case is a good illustration. The only minus is that the part on Bayesian Hypothesis Testing (in particular Bartlett's and Lindley's paradoxes) is a bit rushed up, and not as clear as the rest of the course. All in all, a really good course, I'm glad I followed it.

교육 기관: Do H L

•Jul 09, 2016

I beta-tested this course and it was an amazing experience. The instructors are super engaging and upbeat, despite having to delivery a very complex subject like Bayesian statistics. The quizzes are very rich, with a nice balance of practice quizzes and graded quizzes. The practice quizzes offer very helpful explanations that can help to reinforce understanding before doing the graded quiz. Last but not least, the final project description is super exciting and thorough. I highly look forward to completing this course and get a deep introduction to Bayesian statistics.

교육 기관: Jonathan N

•Oct 23, 2016

Outstanding material. It may be the hardest level compared to the rest specialization course, since Bayes indeed have high technical level detail. But it was worth it. Great course and detailed from the instructors.

교육 기관: Nazir A

•Sep 20, 2019

Excellent coverage. Needed to read up before watching the video in order to be able to follow the concepts. Topic was covered extensively and I was able to learn a whole new way of looking at statistics in general.

교육 기관: Andre G L O

•Feb 05, 2017

For sure the most challenging course so far.

I'm amazed by how our statistical intuition fits with Bayesian approach and how we can get better results.

I'm eager to use this concepts in new models at my job!

교육 기관: Roland

•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.

교육 기관: Graeme H

•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.

교육 기관: Lucía M F

•May 27, 2020

Es un curso muy difícil de seguir si no tienes amplios conocimientos de estadística y probabilidad. Nada que ver con los anteriores de esta especialización.

교육 기관: 魏震

•Nov 17, 2016

Very nice introduction to bayesian statistics, the materials have some level of depth, and the tests and assignments are highly available for beginners.

교육 기관: franco g

•Dec 16, 2016

This is my first course on bayesian statistics, I really like it, it was step by step, and helps to clarify lots of concepts of frequentist statistic.

교육 기관: Shao Y ( H

•Oct 30, 2017

The course is compact that I've learnt a lot of new concepts in a week of coursework. A good sampler of topics related to Bayesian Statistics.

교육 기관: 殷子涵

•Jan 06, 2017

This course is very good for bayesian statistic theory. What is more, it also teaches a lot of coding skills with R which is really useful.

교육 기관: Raffy S C

•Mar 22, 2020

Great course. Quite difficult though. I wished it was split to two course or maybe an entire specialization dedicated for this.

교육 기관: Mark P

•Oct 24, 2017

Slightly math heavy at times but the practical labs were awesome. I thoroughly enjoyed the final modeling assignment as well

교육 기관: Michael B

•Oct 26, 2016

Great course with clear instruction and a final peer-review project with clear expectations and explanations.

교육 기관: Melesio C S

•Aug 22, 2019

It was a very interesting course, i really recommend it if you want to get into bayesian statistics.