Bayesian Statistics: Techniques and Models
FREE
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 …
Instructor Details
Courses : 1
Specification: Bayesian Statistics: Techniques and Models

51 reviews for Bayesian Statistics: Techniques and Models
Add a review Cancel reply
This site uses Akismet to reduce spam. Learn how your comment data is processed.
Price  Free 

Provider  
Duration  36 hours 
Year  2017 
Level  Intermediate 
Language  English 
Certificate  Yes 
Quizzes  Yes 
FREE
Snejana S –
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.
Vithor R F –
Very cool, probably the best course I’ve done in Coursera. Keep rocking! 🙂
Evgenii L –
A very good course to introduce yours
Ahad H T –
Outstanding, Excellent, Must do for statistician. I’m from Civil Engg Background easily capable to learn the course
Sandra M –
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 .
Sathish R –
This course is taught in a way that not useful for real world applications.
Sergio M –
Excelente curso. Da una introduccion a los metodos de MCMC de una forma bastante sencilla y fe acompana en problemas de regresion utilizando JAGS. Recomiendo este curso a todo aquel que tenga nociones de Estadistica Bayesiana, pero que tenga pendiente los metodos avanzados para muestrear la posteriori de los parametros.
Oani d S d C –
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)
Hugo R C R –
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.
Benjamin O A –
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!!!
Victor Z –
A very good practical and theoretical course
Hsiaoyi H –
Great course to learn both theories and techniques!
Nicholas W T –
Very thorough instruction. Excellent feedback and support on forums.
Ilia S –
I found this course very interesting and informative.
Dongliang Y –
Great class.
Ahmed M –
If you want to become good in modelling it is recommended to enrol.
Arnaud D –
Really interesting course. The coding session are useful and can be use cases for lots of various situations.
Wangtx –
Great materials and well organized lecture structure. But in the meanwhile, it requires quite a lot preliminary knowledge.
Jonathan –
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!
Juan C –
Muy recomendable para los investigadores y profesionales que quieren desarrollar productos y procesos nuevos.
Cardy M I –
This course helped me to get some experience at building Bayesian models and how they are applied.
Stephane M –
Good balance between courses and codes exercises
Stephen H –
Fairly good introduction to basic Bayesian statistical models and JAGS, the package to fit those models.
Brian K –
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.
Georgy M –
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!
Nikola M –
one of best stats courses I had
Chen N –
Amazing, super cool!
Lau C –
Super clear and easy to follow. Thanks so much.
Chunhui G –
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.
Tibor R –
Very good and useful course, and hard as well.
Harshit G –
Great course.
Stephen B –
Best course done to date. I wish they had one in STAN too!
Luis A A C –
Excellent course.
Yahia E G –
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.
Eugene B –
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.
dhirendra k –
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
Jiasun –
Not enough depth.
Artem B –
It is very concise, but informative course. It combines both theory and practice in R, which are easy to follow.
Gustavo M –
Very nice course. A bit more theory on sampling methods would be welcome.
Madayan A –
Very good course, a little bit to slow at some point but this is marginal in the overall feeling.
Jayanand S –
Complex subject made easy with easy to understand theory & practical examples
Nancy L –
Thank you!
Hyun J K –
Perfect combination of theory part + application part Recommend to people who took the basic Bayesian class
Arkobrato G –
Great course with challenging assignments and de
Seema K –
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 !
Clement C –
Awsome course overall. I took one star away for the capstone project’s correction system that I think could be improved. If felt this system to be too rigid. Maybe allowing people to give points 1 by 1 intead of just a few options (0, 3 or 5 points) would help. I also feel like too many points are awarded for criterias that are beside the point of the course (5 points for the number of pages, 5 points for knowing how to write an abstract, 3 points for redacting the problem to be answered). This skills however important were not taught in this course and are unfair to evaluate in my opinion.
Krishna D –
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.
Eric A S –
This course gives a very good introduction to Bayesian modeling in R using MCMC.
Ken A –
Excellent course. Streamlined but extremely useful.
Daniele F M –
Classes are very good, but people do not put much effort on peer review coments.
Maxim V –
This course requires quite a lot of preliminary knowledge on the subject. I had to complete the previous course (“Bayesian Statistics: From Concept to Data Analysis”) in order to be able to proceed with this one, and still was apparently missing some essential information towards the end. I would add one more course to fill the gaps and make a specialization out of the three resulting courses.