返回到 Bayesian Statistics: Techniques and Models

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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....

JH

Oct 31, 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!!!

CB

Feb 14, 2021

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.

筛选依据：

创建者 Cooper O

•Aug 1, 2017

This course was fantastic. It combined detailed learning materials with frequent and comprehensive assessments. While managing to cover everything from the basics of MCMC through to the use of a number of different bayesian models. My only issue with the course was that the learning materials encouraged copy-pasting code and often didn't properly explain the choice of priors and other details about the chosen models.

创建者 Paul J

•May 28, 2020

Really a great course! It IS challenging, but the professor does a wonderful job. He also put a lot of thought into helping students learn. For example, when you get an answer wrong on a quiz, each wrong answer has an explanation WHY it was wrong to help you better understand your mistake. And each correct answer also has an explanation :).

创建者 Mr. J

•Apr 30, 2020

Superb.

This course with the MCMC Markov Chain Monte Carlo simulation filled in a critical piece of the statistic puzzle for me. Absolutely brilliant.

A key feature of excellence in the curse is the R code samples that directly parallel the course content. I hope it becomes the new paradigm for all code based instruction on Coursera.

创建者 Danilo I

•Jun 6, 2020

I would say the teachers are amazing. The subject was hard to learn as I'm not in the math and stat field, but I think the explanations were so well constructed that it allowed me to go ahead and even finish a statistical report all by myself. I hope this pair of bayesian teachers don't give up on us and keep doing this amazing job.

创建者 Sergio

•Jun 6, 2018

Excelente curso. Da una introducción a los métodos de MCMC de una forma bastante sencilla y fe acompaña en problemas de regresión utilizando JAGS. Recomiendo este curso a todo aquel que tenga nociones de Estadística Bayesiana, pero que tenga pendiente los métodos avanzados para muestrear la posteriori de los parámetros.

创建者 dhirendra k

•Jul 15, 2019

Very good part II course in continuation with course I. The trainer provided good and detailed explanations throughout the course. Also lot of scenarios covered with help of practical examples. Very much recommended course in Bayesian Theory

创建者 Ujjayini D

•Aug 11, 2020

Wonderful to have a course like this. Thanks to my instructor for being so thorough in teaching the materials and the Capstone project was really helpful to get through it totally. A special thanks to my peers also who reviewed my project.

创建者 Siddaraja D

•May 30, 2020

These 2 courses very good and informative for the one who is new to Bayesian statistics. I liked this course hands on portion in R. it really gave a handle on theory applied in practice. Thanks for making these courses available.

创建者 Samuel Q

•Jan 30, 2021

The instructor is really good. Very engaging and easy to follow. The material itself is heavy on the math but the course lessons are very well structured. The instructor also provides lots of background and recommended reading.

创建者 Maojie T

•Jan 6, 2020

It's good. In this course, professors will guide you on how to build a Bayesian model hand by hand with R. Furthermore, all prior knowledge got from another Bayesian Statistics course can get improved and solid too

创建者 Snejana S

•Apr 5, 2018

This is the most detailed course in practical Bayesian methods that I have seen. I have finally understood concepts I never grasped before. The homework assignments are definitely involved but doable AND enjoyable.

创建者 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.

创建者 Chunhui G

•Apr 18, 2019

This is a great course. Although the first course of this series is lack of organization. But this one is fantastic. The lecturer is great. Although you have to pay money to do the quiz, it is worthwhile.

创建者 Sandip D

•Aug 31, 2020

Just finished this course. This course is very good to learn and provides good insight into MCMC methods and JAGS. A little work is needed from the learner's side for this course to be very successful.

创建者 Jonathan H

•Nov 1, 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!!!

创建者 Curt J B

•Feb 15, 2021

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.

创建者 ANA C F D H

•Sep 21, 2020

It was an excellent course. I feel like I really learned both the theory and the practice using R. I advise everyone who is interested. It's worth it too much.

创建者 Farid M

•May 4, 2020

I really liked the course. It was well organized. The fact that the theory was accompanied by hands-on exercises in R truly reinforced the concept. Well-done!

创建者 ezra k

•Dec 14, 2020

A thorough and comprehensive overview of applied Bayesian modelling which will give you the confidence to start applying Bayesian tools in your own work.

创建者 Cindy W

•Nov 2, 2020

I really enjoy taking this course. I have taken Bayesian course before so this is more like a systematic review for me and I still learned a lot!

创建者 Xi C

•May 9, 2020

Great course. The instructor provided detailed code examples and clear explanations for model intuitions. The final capstone project is a plus.

创建者 Sapientia a D

•Nov 17, 2020

One of the best Bayesian statistics courses. Highly recommend to anyone who wants to learn practical techniques on Bayesian method and models.

创建者 Danial A

•Jan 10, 2018

The best course I had in statistics. unlike many other courses the instructor does not ignore the underlying mathematics of the codes.

创建者 Rishi R

•Sep 1, 2020

One of the best practical math courses present in coursera. Loved the course and will surely look upto the next course eagerly.

创建者 Wangtx

•Dec 11, 2018

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