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学生对 University of California, Santa Cruz 提供的 Bayesian Statistics: Techniques and Models 的评价和反馈

4.8
495 个评分

课程概述

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....

热门审阅

JH

Oct 31, 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!!!

CB

Feb 14, 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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51 - Bayesian Statistics: Techniques and Models 的 75 个评论(共 171 个)

创建者 Arijit D

Aug 22, 2023

I loved the structure very much. At the end we have to do a project and review peer project. This idea is very good. The teaching is also very through and structured.

创建者 ANA C F D H

Sep 21, 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.

创建者 Farid M

May 3, 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!

创建者 Ezra K

Dec 14, 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.

创建者 Yu W

Nov 2, 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!

创建者 Ronnie C

May 9, 2020

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

创建者 Sapientia a D

Nov 17, 2020

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

创建者 Ian K

Aug 27, 2024

This course seems to cover its material clearly, and the material is explained clearly. The quiz/homeworks help to reinforce the lectures.

创建者 Danial A

Jan 10, 2018

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

创建者 Rishi R

Sep 1, 2020

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

创建者 Sandra M D T S

Nov 20, 2023

Good Course which gives knowledge of Bayesian models and Techniques such as MCMC, metropolis hasting and their applications

创建者 Wangtx

Dec 11, 2018

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

创建者 Dongxiao H

Nov 15, 2017

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

创建者 Leonardo F

Apr 2, 2021

Very interesting.

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

创建者 Manuel M S

Aug 20, 2020

Excellent course for introducing yourself to Monte Carlo Methods applied to Bayesian statistics. Highly recommended!

创建者 Ahad H T

May 1, 2018

Outstanding, Excellent, Must do for statistician. I'm from Civil Engg Background easily capable to learn the course

创建者 Russell N

Apr 27, 2020

Fantastic course that I was able to immediately incorporate into my work. Great mix of theory and hands on coding!

创建者 Vlad

Mar 21, 2018

Very good course giving a good practical kickoff to a very interesting and exciting topic of Bayesian statistics.

创建者 KANKAKA E N

Aug 24, 2024

Beautifully structured course, with a good mix of stimulating questions that reinforce learning of the concepts.

创建者 Bill B

Jun 20, 2020

Very useful introduction to practical application of Bayesian inference to real world problems using R and JAGS.

创建者 Artem B

Aug 25, 2019

It is very concise, but informative course. It combines both theory and practice in R, which are easy to follow.

创建者 Ian C

Jun 17, 2020

I really enjoyed the course! Thank you for the very interesting and thought-provoking lectures and assignments.

创建者 Sharang T

Aug 16, 2020

It was a very informative course and it was very useful in giving an introduction to a whole new field for me

创建者 Juan C

Jan 28, 2019

Muy recomendable para los investigadores y profesionales que quieren desarrollar productos y procesos nuevos.

创建者 Ariel A

Aug 28, 2017

This course is a great start for everyone who wants to dive into Bayesian Statistics. Very clear and helpful.