# 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
1,964 个评分
517 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.

## 476 - Bayesian Statistics: From Concept to Data Analysis 的 500 个评论（共 506 个）

Sep 20, 2016

The material is good, but I've found the lectures challenging to understand even having some background in math. It would be good if all the definitions and key facts were stated more prominently in the lectures, as opposed to algebraic transformations which most readers can hopefully do on their own.

Jan 04, 2017

This course requires solid grounding in mathematics. No meant of Social Science graduates without proper training in statistics/mathematics. The course was good in the sense that we could how probability distributions are used to model real world problems.

Study material was certainly not adequate.

Oct 03, 2016

The lectures from week 1 to week 3 are nice and useful to me, but I think there should be more details about the content in week 4. For example, I think the lecture about the Jeffreys prior skipped many things and I did not understand this concept very well.

Nov 29, 2019

Most of the support material should be prior reading. Lecturing could be more useful i.e. explaining ore about why we use certain distribution and how to apply them. Most of it as just reciting formulas and felt like a waste of time...

Jul 01, 2017

It was quite difficult to learn from just the material provided here, and I had to look for info on the web. Also, adding modern real life examples and going into detail would make this course better

Jul 19, 2017

Good course as an introduction to bayesian statistics if you want to pursue more advanced courses in the field or to get some practise working with distributions under the bayesian framework.

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.

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.

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.

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.

Apr 13, 2017

I get lost a bit too often.

The teacher sometimes explains easy concepts and omits the difficult ones (e.g. exponential distribution is explained as "for example if you are waiting for a bus that comes every ten minutes" and then he tells you how to compute expected value and moves on, but he does not say WHAT IT MEANS - is it the probability that I will meet an oncoming bus? is it probability of waiting ten minutes for the bus? is it the average waiting time? is it average number of buses that come every hour? - but there is detailed explanation of what A squared means in lesson two (!))

The teacher often makes me confused as to where he got the numbers he is plugging in the formula or what answer the formula gives.

But I take it as a challenge and I intend to finish the course despite all of that. Sometimes it is fun to decipher the mystic equations. And maybe it is me, maybe I was not born to be a statistician. Maybe there are people that find this stuff easy and understand it right away.

I really like the quizes. They are HARD.

One last thing: Wearing white shirt and using white marker makes it impossible to read what he writes. But I take it is part of the challenge ;-)

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.

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...