Retour à Bayesian Statistics: Techniques and Models

4.8

171 notes

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44 avis

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....

par 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!!!

par B

•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!!!

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43 avis

par Juan Cevallos

•Jan 29, 2019

Muy recomendable para los investigadores y profesionales que quieren desarrollar productos y procesos nuevos.

par Cardy Moten III

•Jan 29, 2019

This course helped me to get some experience at building Bayesian models and how they are applied.

par Jonathan

•Jan 01, 2019

Just finishing this class now......it is very good. Much better than the first one in this series. The videos and examples are better explained, and you leave with a solid understanding of Bayesian Analysis. When I signed up for this class I really wanted to know how I could use tools like MCMC to perform real analysis, and I feel like I got what I signed up for. Well done!

par Wangtx

•Dec 11, 2018

Great materials and well organized lecture structure. But in the meanwhile, it requires quite a lot preliminary knowledge.

par Arnaud Dion

•Dec 08, 2018

Really interesting course. The coding session are useful and can be use cases for lots of various situations.

par Ahmed Mukhtar

•Nov 12, 2018

If you want to become good in modelling it is recommended to enrol.

par Dongliang Yi

•Sep 30, 2018

Great class.

par Ilia Selitcer

•Sep 24, 2018

I found this course very interesting and informative.

par Nicholas William Tomasino

•Sep 06, 2018

Very thorough instruction. Excellent feedback and support on forums.

par Hsiaoyi Hung

•Jul 31, 2018

Great course to learn both theories and techniques!

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