Volver a Bayesian Statistics: Techniques and Models

4.8

247 calificaciones

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68 revisiones

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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por Hsiaoyi H

•Jul 31, 2018

Great course to learn both theories and techniques!

por Harshit G

•May 09, 2019

Great course.

por Lau C

•Apr 15, 2019

Super clear and easy to follow. Thanks so much.

por 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.

por Tibor R

•Apr 20, 2019

Very good and useful course, and hard as well.

por Stephen B

•May 30, 2019

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

por Luis A A C

•Jun 06, 2019

Excellent course.

por 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

por Artem B

•Aug 25, 2019

It is very concise, but informative course. It combines both theory and practice in R, which are easy to follow.

por Gustavo M

•Aug 26, 2019

Very nice course. A bit more theory on sampling methods would be welcome.

por Jayanand S

•Sep 17, 2019

Complex subject made easy with easy to understand theory & practical examples

por Madayan A

•Sep 04, 2019

Very good course, a little bit to slow at some point but this is marginal in the overall feeling.

por 兰茜

•Oct 11, 2019

Thank you!

por Hyun J K

•Oct 13, 2019

Perfect combination of theory part + application part

Recommend to people who took the basic Bayesian class

por Arkobrato G

•Nov 11, 2019

Great course with challenging assignments and de

por Stéphane M

•Feb 25, 2019

Good balance between courses and codes exercises

por Henk v E

•Sep 25, 2017

I thoroughly enjoyed participating in this course, and I do think that I learned a fair number of skills of real conceptual and practical value. Thanks to the instructors' team for their dedicated efforts.

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