Back to Bayesian Statistics: Techniques and Models

4.8

221 ratings

•

61 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....

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!!!

Aug 16, 2019

Very good courses. Maybe a little to slow at some moment (I not saying I understand better than other, I am talking about the rhytm). Otherwise perfect and very useful.

Filter by:

By Sergio M

•Jun 06, 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.

By Vlad V

•Mar 21, 2018

Very good course giving a good practical kickoff to a very interesting and exciting topic of Bayesian statistics.

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 Cooper O

•Aug 02, 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.

By Cameron K

•Jun 07, 2017

An excellent introduction to the rjags package in R and using it to perform Bayesian analysis. The applied learning is supported by lessons in Bayesian theory, however, most of the learning is focussed on fitting, assessing and interpreting Bayesian models using rjags and the rjags language. The course is accessible if you have a passing familiarity with statistics and R. I have used traditional, frequentist statistical techniques for five years and I had no trouble completing this course without having done any Introduction to Bayesian Theory course - just jump right in!

By 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.

By Ariel A

•Aug 28, 2017

This course is a great start for everyone who wants to dive into Bayesian Statistics. Very clear and helpful.

By Luis H

•Jul 30, 2017

Rather useful and easy understanding

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 Ahad H T

•May 02, 2018

Outstanding, Excellent, Must do for statistician. I'm from Civil Engg Background easily capable to learn the course

By Farrukh M

•Jul 25, 2017

I appropriate the way the course is taught.

By Michael B R

•Dec 29, 2017

Great course!

By Jonathan H

•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!!!

By Snejana S

•Apr 05, 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.

By Evgenii L

•May 02, 2018

A very good course to introduce yours

By Thaís P M

•Jul 01, 2017

Very good curse!!

By Jerry L

•Jul 05, 2017

This course fills an essential gap in learning Bayesian statistics, and provides concrete assistance in moving from theory to actual model writing in R and jags. Worth every penny, and then some more. However, the course requires a fairly high level of comfort with both general Bayesian statistics and the R language. I think it would benefit from a brief introductory lecture on jags syntax, as well as some additional worked problem examples.

By Hsiaoyi H

•Jul 31, 2018

Great course to learn both theories and techniques!

By Victor Z

•Jul 30, 2018

A very good practical and theoretical course

By Tibor R

•Apr 20, 2019

Very good and useful course, and hard as well.

By Chunhui G

•Apr 19, 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.

By Harshit G

•May 09, 2019

Great course.

By Stephen B

•May 30, 2019

Best course done to date. I wish they had one in STAN too!

By Luis A A C

•Jun 06, 2019

Excellent course.

By 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

Coursera provides universal access to the world’s best education,
partnering with top universities and organizations to offer courses online.