Just finishing this class now......it is very good. Much better than the first one in this series. The videos and examples are better explained, and you leave with a solid understanding of Bayesian Analysis. When I signed up for this class I really wanted to know how I could use tools like MCMC to perform real analysis, and I feel like I got what I signed up for. Well done!

The course requires good understanding of Bayesian methods and linear modelling, something that is covered in previous course of this track from University of California Santa Cruz.

All quizes are quite easy to complete after watching the videos, but don't be fooled by this apparent simplicity - there is much more to the class than just that.

Capstone project is challenging and does put to test all of the topic discussed in class,

discussion forums are very helpful and also are extremely interesting to read.

I can strongly recommend this class to anyone who is interested in Bayesian Methods.

I've seen quite a few of similar classes on Coursera, but this one is the best, in my opinion, but also is the hardest one.

Do not miss out on Honors track, recommended supplementary reading and Capstone - those are the gems.

Good course, but the peer review process for the Capstone project in Week 5 is broken. Based on submissions to the course Forum in which multiple students have submitted their work on time but not received a grade due to lack of peer reviewers, this has been going on .

Excellent course! This covered a large amount of material, but it was well organized, with a good number of problems to solve. Matthew Heiner does an excellent job with the lectures and explains things well. Coming from the frequentist worldview, I found this course to be a definite challenge, but well worth the time.

I learned a lot about MCMC. This course is taught using R, but I personally was also working on it in python at the same time. I would love to try a higher class. Thank you!

Brilliant course! Very well organized and with useful study cases.Suggestion: It would be nice to have the same examples in Python using, e.g. Stan or PyMC.

really like the content.

the R material in this actually changes my view towards R, so thanks.

This course is a perfect continuation of the Bayesian Statistics course by Prof. Herbert Lee. It's not only mathematically rigorous but also very applied. Excellent for the beginners to the Bayesian Statistics as it allows to start confidently using Bayesian models in practice.

Matthew Heiner is an excellent lecturer. Thank you.

Excellent teacher and very well taught. Right amount of theory and programming combination. Made the subject easy to learn. Enjoyed it very much. Thank you very much.

The course provided a lot of very helpful tools. However, I believe it was a bit too fast paced. Furthermore, there were certain topics which were not explained clearly -- for example, the discussion of the Metropolis-Hastings Algorithm and Gibbs Sampling was extremely confusing.

This course is taught in a way that not useful for real world applications.

Not enough depth.

This was a challenging and excellent course. Every lecture was clear, every quiz question enhanced learning. I felt that the capstone project detracted somewhat from the course but perhaps I chose the wrong problem; I spent several weeks on the project and still felt it was inadequate (I chose a problem from work, which didn't appear to have a very interesting question or solution). I found the explanations on the quizzes to be outstanding; I learned much by reading the explanations, even when I understood the problem and answered correctly. I was very glad to learn about RJAGS and have written my own wrapper for it; it was wonderful to be able to focus more on the theory behind MCMC than on its implementation.

Excellent course by Matthew Heiner. Each module is structured as follows: there is a series of theoretical lessons, where the concepts are explained in a concise but clear and comprehensive way, followed by practical lessons where models are created using JAGS. The teacher provides the code used, and this can be easily reproduced and adapted in your own projects. In addition, the final project provides an opportunity to create your own model to solve issues of your own interest. I highly recommend this course to anyone who shares the philosophy of Bayesian statistics and wants to apply basic models to their own problems of interest.

An excellent introduction to the rjags package in R and using it to perform Bayesian analysis. The applied learning is supported by lessons in Bayesian theory, however, most of the learning is focussed on fitting, assessing and interpreting Bayesian models using rjags and the rjags language. The course is accessible if you have a passing familiarity with statistics and R. I have used traditional, frequentist statistical techniques for five years and I had no trouble completing this course without having done any Introduction to Bayesian Theory course - just jump right in!

This class expands past the concept of Bayesian statistics by getting students to experience what it is like to DO bayesian stats through coding in R. The instructor does an exceptional job of explaining advanced topics without getting too technical and provides great resources for further learning. The cherry on top is synthesizing what we learned in a peer-review project. Overall, to course demands time and is challenging but 10/10 recommend.

The second course of the great series. The knowledge and skills gained in this course allow to actually do statistical analysis on scientific data. The course is very clear, systematic and well presented. Thank you!

This is a great course for an introduction to Bayesian Statistics class. Prior knowledge of the use of R can be very helpful. Thanks for such a wonderful course!!!

One of the best designed courses. The material and videos are very precise and informative. The quiz questions and assignment are very enjoyable. Thank you !

Really interesting course. The coding session are useful and can be use cases for lots of various situations.