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.
Bayesian Statistics: Techniques and Models
About this Course
학습자 경력 결과
완료하는 데 약 36시간 필요
학습자 경력 결과
완료하는 데 약 36시간 필요
UC Santa Cruz is an outstanding public research university with a deep commitment to undergraduate education. It’s a place that connects people and programs in unexpected ways while providing unparalleled opportunities for students to learn through hands-on experience.
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BAYESIAN STATISTICS: TECHNIQUES AND MODELS의 최상위 리뷰
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.
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!!!
One of the best designed courses. The material and videos are very precise and informative. The quiz questions and assignment are very enjoyable. Thank you !
Brilliant course! Very well organized and with useful study cases.Suggestion: It would be nice to have the same examples in Python using, e.g. Stan or PyMC.
The best course I had in statistics. unlike many other courses the instructor does not ignore the underlying mathematics of the codes.
Great materials and well organized lecture structure. But in the meanwhile, it requires quite a lot preliminary knowledge.
Outstanding, Excellent, Must do for statistician. I'm from Civil Engg Background easily capable to learn the course
Very good course giving a good practical kickoff to a very interesting and exciting topic of Bayesian statistics.
It is very concise, but informative course. It combines both theory and practice in R, which are easy to follow.
Fairly good introduction to basic Bayesian statistical models and JAGS, the package to fit those models.
This course helped me to get some experience at building Bayesian models and how they are applied.
Very good course, a little bit to slow at some point but this is marginal in the overall feeling.
This course gives a very good introduction to Bayesian modeling in R using MCMC.
Complex subject made easy with easy to understand theory & practical examples
Very nice course. A bit more theory on sampling methods would be welcome.
Very thorough instruction. Excellent feedback and support on forums.
If you want to become good in modelling it is recommended to enrol.
Best course done to date. I wish they had one in STAN too!
A great course, very detailed and a very good instructor!
I found this course very interesting and informative.
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