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Learner Reviews & Feedback for Bayesian Statistics: Techniques and Models by University of California, Santa Cruz

4.8
221 ratings
61 reviews

About the Course

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....

Top reviews

JH

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!!!

MA

Aug 16, 2019

Very good courses. Maybe a little to slow at some moment (I not saying I understand better than other, I am talking about the rhytm). Otherwise perfect and very useful.

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51 - 60 of 60 Reviews for Bayesian Statistics: Techniques and Models

By Madayan A

Aug 16, 2019

Very good courses. Maybe a little to slow at some moment (I not saying I understand better than other, I am talking about the rhytm). Otherwise perfect and very useful.

By Stéphane M

Feb 25, 2019

Good balance between courses and codes exercises

By zhen w

Jul 28, 2017

really like the content.

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

By 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.

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 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 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.