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
звезд
Оценки: 2,722
Рецензии: 710

## 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
31 авг. 2017 г.

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.

JB
16 окт. 2020 г.

An excellent course with some good hands on exercises in both R and excel. Not for the faint of heart mathematically speaking, assumes a competent understanding of statistics and probability going in

Фильтр по:

## 201–225 из 698 отзывов о курсе Bayesian Statistics: From Concept to Data Analysis

автор: Ariel A

12 окт. 2017 г.

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

5 февр. 2020 г.

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

8 окт. 2018 г.

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

2 февр. 2020 г.

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

автор: Naseera M

12 февр. 2017 г.

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

4 окт. 2018 г.

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

2 мая 2018 г.

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

18 дек. 2016 г.

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

16 янв. 2020 г.

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

автор: Jenna K

13 мая 2019 г.

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

5 апр. 2020 г.

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

автор: Dr. R M

15 нояб. 2017 г.

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

7 июля 2016 г.

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

6 нояб. 2017 г.

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

8 февр. 2017 г.

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

20 янв. 2018 г.

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

автор: Felipe C

13 дек. 2020 г.

Quite interesting course ant not too long. I learnt many interesting and useful concepts in statistics. Highly recommendable.

автор: Jose M R F

14 июля 2019 г.

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

26 сент. 2017 г.

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

автор: Eduardo M

4 янв. 2019 г.

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

автор: 王颖亮

5 авг. 2018 г.

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

автор: Salaheldin G

26 дек. 2017 г.

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

автор: Miles D R

15 авг. 2019 г.

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

автор: Francisco J S G

26 авг. 2018 г.

A really hard course but useful for those who want to know more about statistics and how it is related to Bayes' theorem.

автор: Álvaro Q

27 мар. 2018 г.

It's a good introductory course to Bayesian statistics, a second part with Gibbs Sampling, Markov and MCMC would be nice.