# 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

4.6
2,437 个评分
644 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.

## 601 - Bayesian Statistics: From Concept to Data Analysis 的 625 个评论（共 633 个）

Jul 25, 2020

This course gives a very brief background on conjugate prior. However, the lectures on Bayesian linear regression is too superficial. I wish the lectures could have gone into more detail.

Apr 08, 2020

Too much time spent on the beginning and too little on later more complicated concepts such as the posterior predictive. It felt as if that was just a side note in the extra readings.

Sep 24, 2017

The course is good for beginners in statistics. In my opinion it would be better to invest more time explaining different topics about bayesian regression and bayesian time series.

Jun 01, 2020

Solid mathematical grounding, but would have benefited from more time spent on the history of Bayesian inference, when to use it, why it can be used etc..

Jul 02, 2018

The course could have given more information on tiny details which can confuse people during the exercises. But overall a good learning experience

Jun 30, 2017

We still don't understand how Bayes differs to Frequentist... A worked example comparing the two at the end would have been nice.

May 01, 2019

It would have been great if more graphs had been provided, for easier visualization of the e.g. distributions, or concepts.

Jul 24, 2019

It would be better to add more explain about those equations and connect the math stuffs with the real world samples

Jul 14, 2019

It would be much better if there was a more sufficient introduction to the various distributions used in the course.

Jul 09, 2019

Very informative as an introduction to concepts, but nowhere near the deep dive I'm now interested in taking.

May 04, 2020

Good course!!... Additional examples of real life explained and done in R or excel will make it great

Sep 21, 2019

Too much theoretical than practical applications. No need to give both R and Excel videos.

Nov 26, 2018

Would have liked more problem solving and real-world application examples.

Jun 15, 2020

The workload is manageable however the homework is somewhat challenging.

May 12, 2020

Not well organized.

No sufficient materials, references, etc.

Very short.

May 31, 2018

Overall, it's Ok. but the explanation is too short and incomplete.

Aug 24, 2017

better to come up with more examples and more mathematical details

Jan 01, 2019

This course could be taught in better understanding way

Jun 03, 2019

For some derivations, the explanations are too sparse.

Apr 09, 2017

A bit too short.

Jul 28, 2019

It's alright because it gives you an overview of what is covered in a Bayesian Stats class, but the material is presented quite poorly and I had to do a lot of second hand reading to answer the questions. It is not particularly enlightening even and the formulas are presented without proper grounding, context, and intuition. I can recommend this only for dedicated self-studiers who already have some sort of grounding in Bayesian reasoning.

Feb 02, 2017

Some matters were just given formulas and there was a lack of practice. The course should cover less materials or be longer to be effective in teaching.

Sep 07, 2020

Disappointing. Hard to follow, as concepts are not fully explained or linked. Steps in equations are often skipped without notice.

Jul 04, 2019

it was an okay course, I liked that they used R occasionally in the course, but I did not like how the concepts were discussed

May 01, 2017

Not very much in depth and does not offer complete lecture notes, which are necessary for answering the quizzes...