Voltar para Bayesian Statistics: From Concept to Data Analysis

4.6

1,656 classificações

•

436 avaliações

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.

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por Benjamin A A

•May 21, 2018

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por Kevin P B

•Feb 15, 2018

A good introduction to the concepts conveyed by revealing the equations and expressions on a whiteboard. Minimal work with data and programming - much less of this than other Coursera classes on the same topics. Also unlike other Coursera classes on the same topic, the quiz answers/hints are useful and contain the relevant equations or R commands - not merely "correct" or "you should not have chosen this answer." I found this very helpful for self learning and confirming solution approach.

por Clive S H

•Jan 15, 2018

Interesting, challenging, informative, entertaining, Herbie Lee is an excellent presenter of a very well prepared introduction to what seems to be a more rational and coherent approach to extracting, understanding and evaluating quantative information from data

por Бызов А

•May 27, 2018

Marvellous course! Thank you very much! I would really appreciate, if you'll create an advanced version of this course

por Harsh V D

•Aug 06, 2017

A very well designed and productive course for anyone looking to brush up his/her concepts on Bayesian Statistics

por Soonkyo J

•Feb 18, 2018

The coolest part of this lecture is mathematical explanation of concepts, especially about conjugate priors.

por Xin L

•Mar 31, 2018

Clear and useful. Really helpful.

por Ming L

•May 06, 2018

I like the course and how the questions are designed. I wish it can be accompanied by a booklet of transcripts of some sort. Videos are good, but so are traditional reading materials.

por Ying

•Jun 28, 2017

This is a very useful course for people to do the data analysis in astronomy.

por Tetsuhiko O

•Jan 20, 2018

I studied basic theory from these lectures. I will try again and again until I understand Baysian Statistics concept completely.

por Francisco C

•Sep 22, 2017

Excellent introduction to Bayesian inference. Dr. Lee struck an exceptional balance between presenting concepts and ideas with self-learning through the homework quizzes. I look forward to learning more analysis techniques in subsequent courses!

por Robert K M

•Feb 12, 2018

Invaluable. Excellent quizzes. A few terms could have been better defined, and a few more examples wouldn't hurt, but overall excellent.

por Davide V

•Jan 21, 2017

Short but sweet. This course is a good introduction to the subject. I particularly liked the instructor and the design of the tests, which are really complementary to the learning material and are really helpful to put in practice the somewhat abstract theory. The supplementary material is also well done. It would be nice to have a course book to follow though as referring to videos is not always easy.

por Harold X

•Sep 22, 2016

The course content is concise and straightforward. For novices it is very helpful

por Ali B

•Nov 26, 2017

The course is useful, especially for beginners in stat area.

por Хожиматова К

•Nov 27, 2016

Everything was perfect beginning from explonations and ending with materials

por Pawel B

•Apr 03, 2018

Interesting course providing you with a well-paced start into Bayesian Statistics.

por 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.

por Vinicius P d A

•Apr 19, 2017

Very good!

por Zito R

•Feb 27, 2018

Excellent!

por 思莹 王

•May 08, 2017

It's very amazing!

por 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!

por Zotov A V

•Nov 21, 2016

I want more practice programming tasks for this course.

por 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.

por 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.

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