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学生对 加州大学圣克鲁兹分校 提供的 Bayesian Statistics: From Concept to Data Analysis 的评价和反馈

4.6
1,809 个评分
468 个审阅

课程概述

This course introduces the Bayesian approach to statistics, starting with the concept of probability and moving to the analysis of data. We will learn about the philosophy of the Bayesian approach as well as how to implement it for common types of data. We will compare the Bayesian approach to the more commonly-taught Frequentist approach, and see some of the benefits of the Bayesian approach. In particular, the Bayesian approach allows for better accounting of uncertainty, results that have more intuitive and interpretable meaning, and more explicit statements of assumptions. This course combines lecture videos, computer demonstrations, readings, exercises, and discussion boards to create an active learning experience. For computing, you have the choice of using Microsoft Excel or the open-source, freely available statistical package R, with equivalent content for both options. The lectures provide some of the basic mathematical development as well as explanations of philosophy and interpretation. Completion of this course will give you an understanding of the concepts of the Bayesian approach, understanding the key differences between Bayesian and Frequentist approaches, and the ability to do basic data analyses....

热门审阅

GS

Sep 01, 2017

Good intro to Bayesian Statistics. Covers the basic concepts. Workload is reasonable and quizzes/exercises are helpful. Could include more exercises and additional backgroung/future reading materials.

JH

Jun 27, 2018

Great course. The content moves at a nice pace and the videos are really good to follow. The Quizzes are also set at a good level. You can't pass this course unless you have understood the material.

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176 - Bayesian Statistics: From Concept to Data Analysis 的 200 个评论(共 457 个)

创建者 Fabian S

Jan 18, 2018

A great introduction to Bayesian Statistics for everyone who has some basic knowledge of calculus and is familiar with the fundamentals of probability theory.

创建者 Gu F

Feb 16, 2017

amazing quizes, and you don't have pay to take them.

创建者 Laure N

Mar 06, 2018

Thank you very much for sharing your knowledge with the public. Now I am no more afraid to face the book 'Bayesian Data Analysis' by A. Gelman et al.

创建者 Ariel A

Oct 12, 2017

Great course, it has the right proportion of theory and practice. It's a great start for anyone who wants to dive into Bayesian Analysis.

创建者 Syarif M

Dec 02, 2016

Definitly the best statistic course for beginners with some mathematical knowledge. Love the way the videos are recorded (Transparent glass between the camera and teacher) it should be a standard for online course! thank you so much!

创建者 Jonathan H

Oct 06, 2017

This course is well prepared.

The videos are of high quality and the lessons are easy to follow.

I enjoyed the Honors content as well, that gives an extra challenge to those who want it.

Thanks!

创建者 BaoYiping

Sep 06, 2016

it's very helpful for me to understand the Bayesian statistics. things are clearly stated and the quiz are good. Many thanks! It's better to have a further course on the Monte Carlo. It's better if the regression can be talked more in details.

创建者 Brandon H

Mar 07, 2018

This is a great course! Much better (and cheaper) than the course I took in grad school. Full of practical knowledge, and isn't too overwhelming on the mathematics/statistical theory. It's just right. Good for anyone interested in Bayesian statistics, though some background with probability distributions may help climb the learning curve.

创建者 Vikramnath V

Aug 22, 2016

Excellent lectures by Herbert Lee. Great intuitive content for learners.

创建者 Rigoberto J M A

Nov 06, 2017

Excellent.

创建者 ENRICO S

Aug 17, 2017

Great course. I was more confident in frequentist than Bayesian one so, I found this course very enlightening for me and topics' structure has never been boring.

创建者 Samrat S

Jul 07, 2017

Awesome.. Good overview, but the concepts are much deeper, the second part is also must.

创建者 Xilu W

Nov 20, 2016

I'm a graduate student in mechanical engineering. Thanks for the open course, it is really convenient and helpful!

创建者 Chris

May 14, 2017

great course. gave me a much more intuitive feel and confidence for bayesian analysis.

创建者 Jason R

Apr 08, 2017

I found this course to be incredibly useful to learn Bayesian statistics and a useful guide for applying the information in r and excel. I would definitely recommend it to anyone interested in furthering their understanding on this topic.

创建者 Julian R S

Nov 15, 2017

A great introduction to bayesian statistics. I warmly recommend this course to those already familiar with the frequentist approach and willing to expand their knowledge.

创建者 Vinicius P d A

Apr 19, 2017

Very good!

创建者 Zito R

Feb 27, 2018

Excellent!

创建者 howcanimove@gmail.com

Jan 21, 2017

Very useful lesson! give me a new perspective on daily data analysis.

创建者 Nitin K

Jun 01, 2017

I loved everything about this course. It reminded me of my time in school. Papers and pencils. I look forward to attending the follow up course.

创建者 Zotov A V

Nov 21, 2016

I want more practice programming tasks for this course.

创建者 Galley D

Sep 11, 2017

Outstanding course to understand Bayesian statistics. Teacher is very pedagogical and the course delivery with equations written on the transparent board make everything easy to follow.

As an area for development, I would have like more information on Bayesian linear regression in week 4, through background lecture or dedicated video.

创建者 Kelvin L

May 31, 2017

Very informative and challenging course.

创建者 Ying L

Jul 03, 2017

It's a great course to understand the fundamentals of the Bayesian Statistics. The easy quiz which meant not to deter the students could be improved a bit. For serious learning, reviewing the questions in honor sections and the supplemental materials is a must.

创建者 Georgi S

Sep 01, 2017

Good intro to Bayesian Statistics. Covers the basic concepts. Workload is reasonable and quizzes/exercises are helpful. Could include more exercises and additional backgroung/future reading materials.