Вернуться к Bayesian Statistics: Techniques and Models

4.8

Оценки: 240

•

Рецензии: 66

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!!!

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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автор: Brian K

•Apr 01, 2019

Excellent course! This covered a large amount of material, but it was well organized, with a good number of problems to solve. Matthew Heiner does an excellent job with the lectures and explains things well. Coming from the frequentist worldview, I found this course to be a definite challenge, but well worth the time.

автор: Hugo R C R

•Jun 19, 2018

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.

автор: Georgy M

•Apr 01, 2019

The second course of the great series. The knowledge and skills gained in this course allow to actually do statistical analysis on scientific data. The course is very clear, systematic and well presented. Thank you!

автор: 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!

автор: Arnaud D

•Dec 08, 2018

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

автор: Wangtx

•Dec 11, 2018

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

автор: Juan C

•Jan 29, 2019

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

автор: Cardy M I

•Jan 29, 2019

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

автор: Nikola M

•Apr 07, 2019

one of best stats courses I had

автор: Chen N

•Apr 08, 2019

Amazing, super cool!

автор: Lau C

•Apr 15, 2019

Super clear and easy to follow. Thanks so much.

автор: Stephen H

•Mar 18, 2019

Fairly good introduction to basic Bayesian statistical models and JAGS, the package to fit those models.

автор: Ilia S

•Sep 24, 2018

I found this course very interesting and informative.

автор: Ahmed M

•Nov 12, 2018

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

автор: Dongliang Y

•Sep 30, 2018

Great class.

автор: Nicholas W T

•Sep 06, 2018

Very thorough instruction. Excellent feedback and support on forums.

автор: Dallam M

•Jun 27, 2017

great course

автор: JOSE F

•Feb 11, 2018

Very challenging but interesting!

автор: Víthor R F

•Apr 10, 2018

Very cool, probably the best course I've done in Coursera. Keep rocking! :)

автор: Dongxiao H

•Nov 15, 2017

terrific, so I've learn quite a lot basic knowledge about MCMC. So I can build kinds of models with better understanding.

автор: Yiran W

•Jun 11, 2017

Very helpful!

автор: Юрий Г

•Aug 28, 2017

Excellent course, with deep explanation of difficult topics in Bayesian statistics and Marcov chain applications. Good quizzes and enough time to complete them. Recommend to all interested in probability theory.

автор: Oaní d S d C

•Jun 07, 2018

Excellent course. R usage straight from the beginning, a much useful addition to the previous course. It's very complete and when something mentioned and not explained further additional sources are recommended. Lot's of practical work and the final project I found amazing, a very practical approach that should prepare you to write reports and seriously analyse data. I would just recommend to put in the course prerequisites some basic R and some experience with statistics and probability. Although the course can be taken in isolation, the previous one is almost a prerequisite (if bayes thinking is new to you)

автор: Benjamin O A

•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!!!

автор: nicole s

•Nov 07, 2017

A great course, very detailed and a very good instructor!

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