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學生對 加州大学圣克鲁兹分校 提供的 Bayesian Statistics: Techniques and Models 的評價和反饋

429 個評分
138 條評論


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....



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!!!


The course was really interesting and the codes were easy to follow. Although I did take the previous course for this series, I still found it hard to grasp the concepts immediately.


126 - Bayesian Statistics: Techniques and Models 的 138 個評論(共 138 個)

創建者 Khoa M


The course was really great for me to start using R and build models. But it was really challenging. And the final peer-graded submission really threw me off due to the late / lousy markings for my submission and lack of available submissions for me to mark. Still an enriching and challenging course overall!

創建者 Henk v E


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.

創建者 Eddie G


Very comprehensive and challenging course. The explanations/rationale could be done better In the statistical programming parts.

創建者 Daniele M


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

創建者 Eric A S


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

創建者 Satish C S


The course is very helpful for those who wanted to learn the Bayesian modeling.

創建者 Dziem N


The programming examples are excellent. Thank you...

創建者 Stéphane M


Good balance between courses and codes exercises



I think this course is hard.

創建者 Vittorino M C


I learn a lot, thank you.

創建者 Maxim V


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.

創建者 Andrew M


Learning comes through solving problems. There is way too much information given through videos, wrapped in field specific jargon. I am studying theoretical physics, So I definitely have the background for this class. However, it was too boring to hold my interest and did not provide enough/quality problems to actually help me get good at Bayesian Statistics. Poor MOOC. Good if you just want to put something on your CV and make it look like you know stuff.

創建者 Serum N


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