Back to Bayesian Statistics: Techniques and Models

4.8

stars

313 ratings

•

92 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!!!

Jan 09, 2020

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.

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By dhirendra k

•Jul 15, 2019

Very good part II course in continuation with course I. The trainer provided good and detailed explanations throughout the course. Also lot of scenarios covered with help of practical examples. Very much recommended course in Bayesian Theory

By Siddaraja D

•May 30, 2020

These 2 courses very good and informative for the one who is new to Bayesian statistics. I liked this course hands on portion in R. it really gave a handle on theory applied in practice. Thanks for making these courses available.

By 唐茂杰

•Jan 06, 2020

It's good. In this course, professors will guide you on how to build a Bayesian model hand by hand with R. Furthermore, all prior knowledge got from another Bayesian Statistics course can get improved and solid too

By Snejana S

•Apr 05, 2018

This is the most detailed course in practical Bayesian methods that I have seen. I have finally understood concepts I never grasped before. The homework assignments are definitely involved but doable AND enjoyable.

By Юрий Г

•Aug 28, 2017

Excellent course, with deep explanation of difficult topics in Bayesian statistics and Marcov chain applications. Good quizzes and enough time to complete them. Recommend to all interested in probability theory.

By Chunhui G

•Apr 19, 2019

This is a great course. Although the first course of this series is lack of organization. But this one is fantastic. The lecturer is great. Although you have to pay money to do the quiz, it is worthwhile.

By Jonathan H

•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!!!

By Krishna D

•Jan 09, 2020

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.

By Farid M

•May 04, 2020

I really liked the course. It was well organized. The fact that the theory was accompanied by hands-on exercises in R truly reinforced the concept. Well-done!

By Xi C

•May 10, 2020

Great course. The instructor provided detailed code examples and clear explanations for model intuitions. The final capstone project is a plus.

By Danial A

•Jan 10, 2018

The best course I had in statistics. unlike many other courses the instructor does not ignore the underlying mathematics of the codes.

By Wangtx

•Dec 11, 2018

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

By Dongxiao H

•Nov 15, 2017

terrific, so I've learn quite a lot basic knowledge about MCMC. So I can build kinds of models with better understanding.

By Ahad H T

•May 02, 2018

Outstanding, Excellent, Must do for statistician. I'm from Civil Engg Background easily capable to learn the course

By Russell N

•Apr 27, 2020

Fantastic course that I was able to immediately incorporate into my work. Great mix of theory and hands on coding!

By Vlad V

•Mar 21, 2018

Very good course giving a good practical kickoff to a very interesting and exciting topic of Bayesian statistics.

By William V B

•Jun 20, 2020

Very useful introduction to practical application of Bayesian inference to real world problems using R and JAGS.

By Artem B

•Aug 25, 2019

It is very concise, but informative course. It combines both theory and practice in R, which are easy to follow.

By Ian C

•Jun 17, 2020

I really enjoyed the course! Thank you for the very interesting and thought-provoking lectures and assignments.

By Juan C

•Jan 29, 2019

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

By Ariel A

•Aug 28, 2017

This course is a great start for everyone who wants to dive into Bayesian Statistics. Very clear and helpful.

By Hyun J K

•Oct 13, 2019

Perfect combination of theory part + application part

Recommend to people who took the basic Bayesian class

By Stephen H

•Mar 18, 2019

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

By Cardy M I

•Jan 29, 2019

This course helped me to get some experience at building Bayesian models and how they are applied.

By Madayan A

•Sep 04, 2019

Very good course, a little bit to slow at some point but this is marginal in the overall feeling.

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