# 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

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1,885 个评分
489 条评论

## 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.

## 101 - Bayesian Statistics: From Concept to Data Analysis 的 125 个评论（共 478 个）

Mar 13, 2019

Very good overview to the area. Efficient and clear lectures - emphasis on the quizzes that required just a proper amount of focus and time from my personal point of view.

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.

May 07, 2019

simple, clear and enjoyable. will take the second course in the series, then move to heavy literature on the topic.

Special thank you to the instructor! you are amazing!

Jul 14, 2017

This is a good course. The instructor offers additional material that help with the understanding of the material, along with enough quizzes to help with practical use.

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.

Feb 19, 2019

Great introduction to Bayesian statistics. Very helpful for me, especially for understanding some of the times when priors might be useful, and how they can aid me.

Jul 01, 2017

Taking this course hase been fun. The material is presented in a clear and structured way, the Tests help to understand and deepen the knowledge. I can recommend it.

Aug 27, 2017

very good course with good concept and work. Content is very rich. Assignments are very good. It was very helpful for me. Thanks for providing such a good course.

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.

Aug 01, 2017

Necessary concepts are reviewed to the necessary depth. This is a rigorous yet light material that presents statistics on university intermediate/advanced level.

May 16, 2017

A concise and clear introduction to the Bayesian paradigm. Its conciseness make it suitable for frequentists wanting to get a quick overview of the Bayesian Way.

Dec 02, 2017

Thank you so much, Herbert Lee. I really like the way you explain everything clearly and how you organizes the contents. I recommend this course for my friends.

Dec 29, 2019

The awesome course really liked the mathematically. If someone really want to understand the Bayesian statistics, they should definitely go through this once.

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.

Aug 21, 2017

Great introduction to the Bayesian framework! The exercises are relevant and I look forward to the second part (Bayesian Statistics: Techniques and Models).

Jan 20, 2017

A step by step course, designed to pay attention all the time with tons of practical examples and very clear explanations, I would definitely recommend it!!

Sep 21, 2017

Very good course for fundamentals of Bayesian statistics. Made me understand Monte Hall problem, conditional probability, etc. in a totally different way.

Oct 03, 2016

The course creates great foundations for digging deeper into more complex concepts and trying to run some Bayesian statistics on simple real life problems

Dec 02, 2019

A mathematics course I really enjoyed because the instructor was actually teaching the material as best as one could without meeting the students. Great.

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.

Jul 05, 2017

Great course. Intermediate to advanced level (at least for me). You must have good foundation in probability. If so, you will learn a lot. Thanks

Jun 28, 2017

A Fantastic course. Detailed learning materials, Lots of opportunities to test your knowledge, and difficult enough to make you learn something!

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.

Mar 11, 2019

It's a good course to know the principal concepts of Bayesian statistics. Also, the course has excellent examples to understand thew concepts.

Mar 02, 2018

I really appreciated the content, and the way it was taught by Prof. Lee. His explanations were intuitive, without loss of mathematical rigour.