Back to Bayesian Statistics: Techniques and Models

4.8

231 ratings

•

64 reviews

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....

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!!!

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.

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By 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.

By Arnaud D

•Dec 08, 2018

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

By 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!

By 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!

By 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.

By Wangtx

•Dec 11, 2018

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

By Juan C

•Jan 29, 2019

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

By Cardy M I

•Jan 29, 2019

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

By Ahmed M

•Nov 12, 2018

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

By Stephen H

•Mar 18, 2019

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

By Victor Z

•Jul 30, 2018

A very good practical and theoretical course

By Hsiaoyi H

•Jul 31, 2018

Great course to learn both theories and techniques!

By Nicholas W T

•Sep 06, 2018

Very thorough instruction. Excellent feedback and support on forums.

By Ilia S

•Sep 24, 2018

I found this course very interesting and informative.

By Dongliang Y

•Sep 30, 2018

Great class.

By Vladimir Y

•Nov 11, 2017

The course requires good understanding of Bayesian methods and linear modelling, something that is covered in previous course of this track from University of California Santa Cruz.

All quizes are quite easy to complete after watching the videos, but don't be fooled by this apparent simplicity - there is much more to the class than just that.

Capstone project is challenging and does put to test all of the topic discussed in class,

discussion forums are very helpful and also are extremely interesting to read.

I can strongly recommend this class to anyone who is interested in Bayesian Methods.

I've seen quite a few of similar classes on Coursera, but this one is the best, in my opinion, but also is the hardest one.

Do not miss out on Honors track, recommended supplementary reading and Capstone - those are the gems.

By nicole s

•Nov 07, 2017

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

By Michael B R

•Dec 29, 2017

Great course!

By Víthor R F

•Apr 10, 2018

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

By Igor K

•Jun 13, 2017

This course is a perfect continuation of the Bayesian Statistics course by Prof. Herbert Lee. It's not only mathematically rigorous but also very applied. Excellent for the beginners to the Bayesian Statistics as it allows to start confidently using Bayesian models in practice.

Matthew Heiner is an excellent lecturer. Thank you.

By 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)

By Dallam M

•Jun 27, 2017

great course

By JOSE F

•Feb 11, 2018

Very challenging but interesting!

By 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.

By Yiran W

•Jun 11, 2017

Very helpful!