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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

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!

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26 - 50 de 130 revisiones para Bayesian Statistics: Techniques and Models

por Sergio

6 de jun. de 2018

Excelente curso. Da una introducción a los métodos de MCMC de una forma bastante sencilla y fe acompaña en problemas de regresión utilizando JAGS. Recomiendo este curso a todo aquel que tenga nociones de Estadística Bayesiana, pero que tenga pendiente los métodos avanzados para muestrear la posteriori de los parámetros.

por dhirendra k

15 de jul. de 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 Ujjayini D

11 de ago. de 2020

Wonderful to have a course like this. Thanks to my instructor for being so thorough in teaching the materials and the Capstone project was really helpful to get through it totally. A special thanks to my peers also who reviewed my project.

por Siddaraja D

30 de may. de 2020

These 2 courses very good and informative for the one who is new to Bayesian statistics. I liked this course hands on portion in R. it really gave a handle on theory applied in practice. Thanks for making these courses available.

por Samuel Q

30 de ene. de 2021

The instructor is really good. Very engaging and easy to follow. The material itself is heavy on the math but the course lessons are very well structured. The instructor also provides lots of background and recommended reading.

por Maojie T

6 de ene. de 2020

It's good. In this course, professors will guide you on how to build a Bayesian model hand by hand with R. Furthermore, all prior knowledge got from another Bayesian Statistics course can get improved and solid too

por Snejana S

5 de abr. de 2018

This is the most detailed course in practical Bayesian methods that I have seen. I have finally understood concepts I never grasped before. The homework assignments are definitely involved but doable AND enjoyable.

por Юрий Г

28 de ago. de 2017

Excellent course, with deep explanation of difficult topics in Bayesian statistics and Marcov chain applications. Good quizzes and enough time to complete them. Recommend to all interested in probability theory.

por Chunhui G

18 de abr. de 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 Sandip D

31 de ago. de 2020

Just finished this course. This course is very good to learn and provides good insight into MCMC methods and JAGS. A little work is needed from the learner's side for this course to be very successful.

por Jonathan H

1 de nov. 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!!!

por Curt J B

15 de feb. 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.

por Toshiaki O

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!

por Krishna D

9 de ene. de 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.

por ANA C F D H

21 de sep. de 2020

It was an excellent course. I feel like I really learned both the theory and the practice using R. I advise everyone who is interested. It's worth it too much.

por Farid M

4 de may. de 2020

I really liked the course. It was well organized. The fact that the theory was accompanied by hands-on exercises in R truly reinforced the concept. Well-done!

por Ezra K

14 de dic. de 2020

A thorough and comprehensive overview of applied Bayesian modelling which will give you the confidence to start applying Bayesian tools in your own work.

por Yu W

2 de nov. de 2020

I really enjoy taking this course. I have taken Bayesian course before so this is more like a systematic review for me and I still learned a lot!

por Xi C

9 de may. de 2020

Great course. The instructor provided detailed code examples and clear explanations for model intuitions. The final capstone project is a plus.

por Sapientia a D

17 de nov. de 2020

One of the best Bayesian statistics courses. Highly recommend to anyone who wants to learn practical techniques on Bayesian method and models.

por Danial A

10 de ene. de 2018

The best course I had in statistics. unlike many other courses the instructor does not ignore the underlying mathematics of the codes.

por Rishi R

1 de sep. de 2020

One of the best practical math courses present in coursera. Loved the course and will surely look upto the next course eagerly.

por Wangtx

11 de dic. de 2018

Great materials and well organized lecture structure. But in the meanwhile, it requires quite a lot preliminary knowledge.

por Dongxiao H

15 de nov. de 2017

terrific, so I've learn quite a lot basic knowledge about MCMC. So I can build kinds of models with better understanding.

por Leonardo F

2 de abr. de 2021

Very interesting.

I would like to have a follow on since the possible applications of the topics explained in the course.