Retour à Bayesian Statistics: Techniques and Models

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142 avis

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

31 oct. 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

14 févr. 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.

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par Jonathan B

•1 janv. 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!

par Sandra M

•14 mai 2018

Good course, but the peer review process for the Capstone project in Week 5 is broken. Based on submissions to the course Forum in which multiple students have submitted their work on time but not received a grade due to lack of peer reviewers, this has been going on .

par Milo V

•19 juin 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.

par Brian K

•1 avr. 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.

par zhen w

•28 juil. 2017

really like the content.

the R material in this actually changes my view towards R, so thanks.

par Vladimir Y

•11 nov. 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.

par Igor K

•12 juin 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.

par Eugene B

•26 juin 2019

The course provided a lot of very helpful tools. However, I believe it was a bit too fast paced. Furthermore, there were certain topics which were not explained clearly -- for example, the discussion of the Metropolis-Hastings Algorithm and Gibbs Sampling was extremely confusing.

par Sathishkumar R P

•21 mai 2018

This course is taught in a way that not useful for real world applications.

par Jiasun L

•20 juil. 2019

Not enough depth.

par Paolo P

•26 mars 2022

Excellent course by Matthew Heiner. Each module is structured as follows: there is a series of theoretical lessons, where the concepts are explained in a concise but clear and comprehensive way, followed by practical lessons where models are created using JAGS. The teacher provides the code used, and this can be easily reproduced and adapted in your own projects. In addition, the final project provides an opportunity to create your own model to solve issues of your own interest. I highly recommend this course to anyone who shares the philosophy of Bayesian statistics and wants to apply basic models to their own problems of interest.

par Cameron K

•7 juin 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!

par Tracey

•6 oct. 2020

This class expands past the concept of Bayesian statistics by getting students to experience what it is like to DO bayesian stats through coding in R. The instructor does an exceptional job of explaining advanced topics without getting too technical and provides great resources for further learning. The cherry on top is synthesizing what we learned in a peer-review project. Overall, to course demands time and is challenging but 10/10 recommend.

par Georgy M

•1 avr. 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!

par Toshiaki O

•23 nov. 2020

I learned a lot about MCMC. This course is taught using R, but I personally was also working on it in python at the same time. I would love to try a higher class. Thank you!

par Krishna D

•9 janv. 2020

Excellent teacher and very well taught. Right amount of theory and programming combination. Made the subject easy to learn. Enjoyed it very much. Thank you very much.

par Benjamin O A

•7 juil. 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!!!

par Seema K

•17 nov. 2019

One of the best designed courses. The material and videos are very precise and informative. The quiz questions and assignment are very enjoyable. Thank you !

par Elibre

•8 déc. 2018

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

par Yahia E

•6 juin 2019

Really good intermediate introduction to bayesian analysis. I really liked how hands-on the course is. The last project was very useful as one will likely to face challenges and try to solve them especially if you use a rich dataset.

par Chiu W K

•29 juil. 2017

Informative but the pace is slow

par Andrew M

•8 nov. 2021

Learning comes through solving problems. There is way too much information given through videos, wrapped in field specific jargon. I am studying theoretical physics, So I definitely have the background for this class. However, it was too boring to hold my interest and did not provide enough/quality problems to actually help me get good at Bayesian Statistics. Poor MOOC. Good if you just want to put something on your CV and make it look like you know stuff.

par Oaní d S d C

•7 juin 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)

par Jens K

•13 juin 2020

This course is fantastic. The presenter is talking slowly and concise, and doesn't shy away from letting the simple things sink in before moving on. At the end it'll get complicated, sure, but this course takes the time every piece needs, where one thing builds on another. I'm glad I made it through the prerequisite course, which was rough. I've had some personal delays throughout this course, but it was easy to re-visit past lectures to refresh and move on. Very well didactically prepared and presented.

par Jerry L

•5 juil. 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.

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