返回到 Bayesian Statistics: Techniques and Models

4.8

239 个评分

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66 个审阅

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!!!

筛选依据：

创建者 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.

创建者 Gustavo M

•Aug 26, 2019

Very nice course. A bit more theory on sampling methods would be welcome.

创建者 Jayanand S

•Sep 17, 2019

Complex subject made easy with easy to understand theory & practical examples

创建者 Madayan A

•Sep 04, 2019

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

创建者 兰茜

•Oct 11, 2019

Thank you!

创建者 Hyun J K

•Oct 13, 2019

Perfect combination of theory part + application part

Recommend to people who took the basic Bayesian class

创建者 Stéphane M

•Feb 25, 2019

Good balance between courses and codes exercises

创建者 zhen w

•Jul 28, 2017

really like the content.

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

创建者 Henk v E

•Sep 25, 2017

I thoroughly enjoyed participating in this course, and I do think that I learned a fair number of skills of real conceptual and practical value. Thanks to the instructors' team for their dedicated efforts.

创建者 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.

创建者 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.

创建者 Chiu W K

•Jul 29, 2017

Informative but the pace is slow

创建者 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 .

创建者 Sathish R

•May 21, 2018

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

创建者 Jiasun

•Jul 20, 2019

Not enough depth.