Back to Bayesian Statistics: Techniques and Models

4.8

247 ratings

•

68 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!!!

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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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!

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 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 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 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 Farrukh M

•Jul 25, 2017

I appropriate the way the course is taught.

By Luis H

•Jul 30, 2017

Rather useful and easy understanding

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 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 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.

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 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 Lau C

•Apr 15, 2019

Super clear and easy to follow. Thanks so much.

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 Tibor R

•Apr 20, 2019

Very good and useful course, and hard as well.

By Chen N

•Apr 08, 2019

Amazing, super cool!

By Nikola M

•Apr 07, 2019

one of best stats courses I had

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