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Вернуться к Bayesian Statistics: From Concept to Data Analysis

Отзывы учащихся о курсе Bayesian Statistics: From Concept to Data Analysis от партнера Калифорнийский университет в Санта-Крузе

4.6
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Оценки: 2,350
Рецензии: 619

О курсе

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....

Лучшие рецензии

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.

Фильтр по:

176–200 из 607 отзывов о курсе Bayesian Statistics: From Concept to Data Analysis

автор: Michael W

Jan 16, 2019

Great introductory course. It was challenging but doable for someone who has not take college level mathematics or statistics in a few years.

автор: 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.

автор: Damian C

Nov 10, 2016

Very well presented course. Interesting and intuitive introduction into the fascinating Bayesian world.

Many thanks and congratulations!!!

автор: 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.

автор: Hari S

Feb 05, 2020

Thought is a simple manner. Made complex concepts look very easy. Would surely recommend this course. Thanks Prof. Herbert Lee and team.

автор: Vignesh R

Oct 08, 2018

Awesome course that helped me overcome the Bayesian statistics way of thinking hurdle. Now, I want to go on and learn MCMC, Metropolis !

автор: Qinyu X

Feb 02, 2020

The course is generally great. Nonetheless, it is not recommended for those without a statistical background and knowledge of calculus.

автор: Naseera M

Feb 12, 2017

Very good course. Prof. Lee explains each concept well. Bayesian Stats makes more sense to me now than before!!

Thanks so much Prof. Lee

автор: Gustavo C

Oct 04, 2018

I loved this course, I learned a lot and I hope I will be able to use this knowledge when I go back to college for my Master's degree.

автор: Evgenii L

May 02, 2018

A very good course. Even better if you continue with the 2nd course that teaches about how to implement Bayesian data analysis in JAGS

автор: Joseph G

Dec 18, 2016

I enjoyed the lecturer, the material is relevant, and the tests are well tailored to ensure you are absorbing the correct information.

автор: Rodrigo G

Jan 16, 2020

Give you great insight. Very intuitive. Although we went through the last week rather quick (more explanation would have been better)

автор: Jenna K

May 13, 2019

The lectures are at the right pace; concise and challenging. Great examples. Thank you so much for providing us with great materials.

автор: Matthew S

Apr 05, 2020

Pretty challenging course. Well organized and well delivered. I learned from the exercises and also the feedback from the exercises.

автор: Dr. R M

Nov 15, 2017

Very informative and clear presentation of the material, which makes it fun and quick to learn the topics. Very good quiz questions.

автор: 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.

автор: Humberto R C

Nov 06, 2017

A clear and compact introduction. Quizzes and exercises are relevant. I got acces to grades and feedback in the audit one I took.

автор: Raj s

Feb 09, 2017

Learned something new :). Lecture were excellent, but, I need time to digest and hope I will get opportunity to use it in future.

автор: 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.

автор: Jose M R F

Jul 14, 2019

Very well explained. Lectures are given in a very nice way as the professor writes. Exercises and quizzes are very well done.

автор: Zhirui W

Sep 26, 2017

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

автор: Eduardo S P R M

Jan 04, 2019

Very good material! The Prof explains very easily the contents of the course. Great course! I recommend. E. Martins, Brazil

автор: Leon W

Aug 05, 2018

The video content is not too much. However, students can learn and practise a lot from supplementary materials and quizzes.

автор: Salaheldin G

Dec 26, 2017

Very useful crash course in Bayesian Statistics. It requires some basic knowledge in statistics and probability as stated.

автор: Miles D R

Aug 15, 2019

This course was dense, concise, and yet easy to follow for individuals that are fairly comfortable with basic statistics.