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Opiniones y comentarios de aprendices correspondientes a Bayesian Statistics: Techniques and Models por parte de Universidad de California en Santa Cruz

4.8
estrellas
400 calificaciones
131 reseña

Acerca del Curso

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....

Principales reseñas

JH
31 de oct. de 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!!!

TO
23 de nov. de 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!

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126 - 130 de 130 revisiones para Bayesian Statistics: Techniques and Models

por Stéphane M

25 de feb. de 2019

Good balance between courses and codes exercises

por SANDRA H M

17 de jul. de 2020

I think this course is hard.

por Vittorino M C

31 de jul. de 2020

I learn a lot, thank you.

por Maxim V

13 de feb. de 2020

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.

por Serum N

26 de feb. de 2020

Such shallow course. You will be better off reading chapter1 of Bayesian data analysis. Don't waste your time here.