Chevron Left
Volver a Bayesian Statistics: Techniques and Models

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

399 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

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!!!

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!

Filtrar por:

101 - 125 de 130 revisiones para Bayesian Statistics: Techniques and Models

por Ge T

13 de feb. de 2021

Easy to follow. Great content.


8 de sep. de 2020


por Chen N

8 de abr. de 2019

Amazing, super cool!

por Luis A

6 de jun. de 2019

Excellent course.

por Thais P

1 de jul. de 2017

Very good curse!!

por Neha K

14 de sep. de 2020

excellent course

por sameen n

30 de abr. de 2020

Amazing course.

por Harshit G

9 de may. de 2019

Great course.

por Michael B R

29 de dic. 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 sep. de 2018

Great class.

por Dallam M

27 de jun. de 2017

great course


25 de dic. de 2020

Good Work!

por Nancy L

11 de oct. de 2019

Thank you!

por Owendrila S

28 de sep. de 2020

Very Good


9 de ago. de 2020


por Md. R Q S

23 de sep. de 2020


por MD F K

27 de ago. de 2020


por Clément C

13 de dic. 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 sep. 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 ene. de 2021

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

por Daniele M

11 de feb. de 2020

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

por Eric A S

12 de ene. 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...