Back to Bayesian Statistics: Techniques and Models

4.8

248 ratings

•

69 reviews

This is the second of a two-course sequence introducing the fundamentals of Bayesian statistics. It builds on the course Bayesian Statistics: From Concept to Data Analysis, which introduces Bayesian methods through use of simple conjugate models. Real-world data often require more sophisticated models to reach realistic conclusions. This course aims to expand our “Bayesian toolbox” with more general models, and computational techniques to fit them. In particular, we will introduce Markov chain Monte Carlo (MCMC) methods, which allow sampling from posterior distributions that have no analytical solution. We will use the open-source, freely available software R (some experience is assumed, e.g., completing the previous course in R) and JAGS (no experience required). We will learn how to construct, fit, assess, and compare Bayesian statistical models to answer scientific questions involving continuous, binary, and count data. This course combines lecture videos, computer demonstrations, readings, exercises, and discussion boards to create an active learning experience. The lectures provide some of the basic mathematical development, explanations of the statistical modeling process, and a few basic modeling techniques commonly used by statisticians. Computer demonstrations provide concrete, practical walkthroughs. Completion of this course will give you access to a wide range of Bayesian analytical tools, customizable to your data....

Nov 01, 2017

This course is excellent! The material is very very interesting, the videos are of high quality and the quizzes and project really helps you getting it together. I really enjoyed it!!!

Jul 08, 2018

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!!!

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By Brian K

•Apr 01, 2019

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.

By Jonathan

•Jan 01, 2019

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!

By Hugo R C R

•Jun 19, 2018

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.

By Arnaud D

•Dec 08, 2018

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

By Seema K

•Nov 17, 2019

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 !

By Georgy M

•Apr 01, 2019

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!

By zhen w

•Jul 28, 2017

really like the content.

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

By Eugene B

•Jun 26, 2019

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.

By Yahia E G

•Jun 06, 2019

Really good intermediate introduction to bayesian analysis. I really liked how hands-on the course is. The last project was very useful as one will likely to face challenges and try to solve them especially if you use a rich dataset.

By Chiu W K

•Jul 29, 2017

Informative but the pace is slow

By Sandra M

•May 14, 2018

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 .

By Sathish R

•May 21, 2018

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

By Jiasun

•Jul 20, 2019

Not enough depth.

By Wangtx

•Dec 11, 2018

Great materials and well organized lecture structure. But in the meanwhile, it requires quite a lot preliminary knowledge.

By Juan C

•Jan 29, 2019

Muy recomendable para los investigadores y profesionales que quieren desarrollar productos y procesos nuevos.

By Nikola M

•Apr 07, 2019

one of best stats courses I had

By Chen N

•Apr 08, 2019

Amazing, super cool!

By Lau C

•Apr 15, 2019

Super clear and easy to follow. Thanks so much.

By Stephen H

•Mar 18, 2019

Fairly good introduction to basic Bayesian statistical models and JAGS, the package to fit those models.

By Ilia S

•Sep 24, 2018

I found this course very interesting and informative.

By Ahmed M

•Nov 12, 2018

If you want to become good in modelling it is recommended to enrol.

By Dongliang Y

•Sep 30, 2018

Great class.

By Nicholas W T

•Sep 06, 2018

Very thorough instruction. Excellent feedback and support on forums.

By Dallam M

•Jun 27, 2017

great course

By JOSE F

•Feb 11, 2018

Very challenging but interesting!

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