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Voltar para Bayesian Statistics: Techniques and Models

Comentários e feedback de alunos de Bayesian Statistics: Techniques and Models da instituição Universidade da Califórnia, Santa Cruz

409 classificações
131 avaliações

Sobre o 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....

Melhores avaliações

31 de Out 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!!!

14 de Fev de 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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101 — 125 de 130 Avaliações para o Bayesian Statistics: Techniques and Models

por Ge T

13 de Fev de 2021

Easy to follow. Great content.


8 de Set de 2020


por Chen N

8 de Abr de 2019

Amazing, super cool!

por Luis A A C

6 de Jun de 2019

Excellent course.

por Thais P

1 de Jul de 2017

Very good curse!!

por Neha K

14 de Set de 2020

excellent course

por sameen n

30 de Abr de 2020

Amazing course.

por Harshit G

9 de Mai de 2019

Great course.

por Michael B R

29 de Dez de 2017

Great course!

por Yiran W

11 de Jun de 2017

Very helpful!

por Aya M L N

9 de Nov de 2020

Thanks a lot

por Dongliang Y

30 de Set de 2018

Great class.

por Dallam M

27 de Jun de 2017

great course


25 de Dez de 2020

Good Work!

por Nancy L

11 de Out de 2019

Thank you!

por Owendrila S

28 de Set de 2020

Very Good


9 de Ago de 2020


por Md. R Q S

23 de Set de 2020


por MD F K

27 de Ago de 2020


por Clément C

13 de Dez de 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

25 de Set de 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 Eddie G

21 de Jan de 2021

Very comprehensive and challenging course. The explanations/rationale could be done better In the statistical programming parts.

por Daniele M

11 de Fev de 2020

Classes are very good, but people do not put much effort on peer review coments.

por Eric A S

12 de Jan de 2020

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

por Dziem N

22 de Jun de 2020

The programming examples are excellent. Thank you...