返回到 Bayesian Statistics: From Concept to Data Analysis

4.6

1,809 个评分

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468 个审阅

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....

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.

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.

筛选依据：

创建者 Quan N

•Aug 02, 2017

This course helped me a lot in getting a better understanding of Bayesian methods. I recommend this course for all data scientists and machine learning practitioners.

创建者 Marcin K

•Sep 23, 2017

I took this course due to my interest in machine learning and graphical models. I like the approach and execution. I recommend it for anyony interrested in statistical inference. Some topics require looking up external sources, like wikipedia, but it is not an issue.

创建者 Fabian M

•Feb 20, 2018

The course manages very well to balance out comprehensibility and content. Professor Herbert Lee has obviously prepared the material very thoroughly and imparts the content of the course in an enjoyable fashion.

创建者 Liublu B

•Aug 29, 2017

Very good, I reccomend it to data scientist

创建者 Tomáš B

•Sep 08, 2016

Short, simple and clear explanations. I only regret the course is not longer.

创建者 Mas N

•Jan 18, 2017

Excellent ! Thank you so much !

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 Sujith N

•Feb 24, 2018

As a primer to Bayesian Statistics, this course covers the basics at a brisk pace. No time is wasted in explaining the basics of Probability theory; which I have always found, at best, to be distracting in the other similar courses I have taken. Thank you, Herbert Lee and Coursera.

创建者 Zhirui W

•Sep 26, 2017

Become very clear about all the formula and derivation of Bayesian Statistics after taking this course. Strongly recommended.

创建者 Nguyen Q V

•Aug 21, 2017

It is the good place to start to learn Bayesian theory

创建者 Bijit D

•Sep 20, 2017

A great course on Bayesian Statistics.

创建者 Fedor T

•Jan 21, 2017

Very clear lectures masterfully delivered by prof. Lee. The quizzes are good, if somewhat on the easy side. Don't be discouraged by the choice of R as the tool for assignments. R is flawed as a programming language, but you won't need to do any programming, only one-liners to evaluate various statistical functions and plot results.

创建者 Nathaniel R

•Nov 21, 2016

This is the first online course I have ever taken so I don't have anything to compare it to, but this course was excellent! The lectures and materials were very clear and I will be adopting some of Prof. Lee's approach into my own teaching practice. The bar has been set very high for any future online courses that I will take!

创建者 Xiaoyang G

•Jul 07, 2016

This course is a very good introductory of bayesian statistics. But it better that you have known the basic statistics inference.

创建者 Andreas Z

•Mar 08, 2018

Very good introduction to Bayesian Statistics.

创建者 Angelo A d M F

•Jan 09, 2017

Excellent introductory course to bayesian statistics. I'd like to thank Professor Lee, University of Santa Cruz, Coursera and all supporting staff for the opportunity. I'd enjoy if you provided intermediate and advanced courses on bayesian statistics that covers more topics.

创建者 Thomas E

•Aug 31, 2016

I followed this class with a great enthousiasm. It was very clear and pedagogical !

创建者 张宁

•Sep 24, 2016

This course are excellent and Thanks for Prof for offering the course. I've learned a lot from the course. Thank you.

创建者 Jinxiao Z

•Jun 21, 2018

excellent

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 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 Vikramnath V

•Aug 22, 2016

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

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 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 kacl780tr

•Jul 06, 2017

Excellent course, although it would have been nice to get more content on uninformative priors and Fisher information.