Voltar para Bayesian Statistics: Techniques and Models

4.8

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259 classificações

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72 avaliações

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!!!

Jan 09, 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.

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por Lau C

•Apr 15, 2019

Super clear and easy to follow. Thanks so much.

por Tibor R

•Apr 20, 2019

Very good and useful course, and hard as well.

por Victor Z

•Jul 30, 2018

A very good practical and theoretical course

por Farrukh M

•Jul 25, 2017

I appropriate the way the course is taught.

por Evgenii L

•May 02, 2018

A very good course to introduce yours

por Luis H

•Jul 30, 2017

Rather useful and easy understanding

por JOSE F

•Feb 11, 2018

Very challenging but interesting!

por Nikola M

•Apr 07, 2019

one of best stats courses I had

por Chen N

•Apr 08, 2019

Amazing, super cool!

por Luis A A C

•Jun 06, 2019

Excellent course.

por Thaís P M

•Jul 01, 2017

Very good curse!!

por Harshit G

•May 09, 2019

Great course.

por Michael B R

•Dec 29, 2017

Great course!

por Yiran W

•Jun 11, 2017

Very helpful!

por Dongliang Y

•Sep 30, 2018

Great class.

por Dallam M

•Jun 27, 2017

great course

por Nancy L

•Oct 11, 2019

Thank you!

por Clément C

•Dec 13, 2019

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.

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.

por Eric A S

•Jan 12, 2020

This course gives a very good introduction to Bayesian modeling in R using MCMC.

por Stéphane M

•Feb 25, 2019

Good balance between courses and codes exercises

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