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学生对 加州大学圣克鲁兹分校 提供的 Bayesian Statistics: Techniques and Models 的评价和反馈

313 个评分
92 条评论


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.


76 - Bayesian Statistics: Techniques and Models 的 91 个评论(共 91 个)

创建者 Thaís P M

Jul 01, 2017

Very good curse!!

创建者 Sameen N

Apr 30, 2020

Amazing course.

创建者 Harshit G

May 09, 2019

Great course.

创建者 Michael B R

Dec 29, 2017

Great course!

创建者 Yiran W

Jun 11, 2017

Very helpful!

创建者 Dongliang Y

Sep 30, 2018

Great class.

创建者 Dallam M

Jun 27, 2017

great course

创建者 Nancy L

Oct 11, 2019

Thank you!

创建者 Clément C

Dec 13, 2019

Awsome course overall. I took one star away for the capstone project's correction system that I think could be improved. If felt this system to be too rigid. Maybe allowing people to give points 1 by 1 intead of just a few options (0, 3 or 5 points) would help. I also feel like too many points are awarded for criterias that are beside the point of the course (5 points for the number of pages, 5 points for knowing how to write an abstract, 3 points for redacting the problem to be answered). This skills however important were not taught in this course and are unfair to evaluate in my opinion.

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 Daniele F M

Feb 11, 2020

Classes are very good, but people do not put much effort on peer review coments.

创建者 Eric A S

Jan 12, 2020

This course gives a very good introduction to Bayesian modeling in R using MCMC.

创建者 Dziem N

Jun 22, 2020

The programming examples are excellent. Thank you...

创建者 Stéphane M

Feb 25, 2019

Good balance between courses and codes exercises

创建者 Maxim V

Feb 14, 2020

This course requires quite a lot of preliminary knowledge on the subject. I had to complete the previous course ("Bayesian Statistics: From Concept to Data Analysis") in order to be able to proceed with this one, and still was apparently missing some essential information towards the end. I would add one more course to fill the gaps and make a specialization out of the three resulting courses.

创建者 Serum N

Feb 27, 2020

Such shallow course. You will be better off reading chapter1 of Bayesian data analysis. Don't waste your time here.