# 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
1,915 个评分
500 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.

## 451 - Bayesian Statistics: From Concept to Data Analysis 的 475 个评论（共 490 个）

Sep 20, 2017

Very good introduction!

Jun 06, 2017

I am working in the field of epidemiological, medical research. Overall I would recommend taking this course. It needs to be pointed out, however, that if you are outside of the field of mathematics this specific course entails a lot of research (using google etc) that needs to be undertaken to understand the course material. Maybe in the future the course directors can compile a summary of all important formulae etc so that professionals from sectors other than mathematics can follow more easily and can focus much on this particular course on Bayesian statistics and not so much on conducting additional research to understand the basic course material. Furthermore, alongside a summary formula sheet it would be good to have all explanations included, what the parameters (alpha, beta etc) stand for with regards to the specific context. Thank you very much for this course!

Oct 22, 2019

This course provides a good overview to Bayesian statistics, but a larger dose of explanations of would be very useful. Mr Lee discusses, in the beginning, the differences between frequentist and bayesian paradigm. I feel that this would be beneficial in the other parts of the course, too. I feel that many of the lectures simply go too fast. After lectures full of Math, it would be useful to present lectures analyzing what had just been taught, in order to better grasp the content. And in general, this happens through the whole course - most lectures are basically math, without much time for grasping the intuition and underlying logic. For example: in the final part, under linear regression, it might be be difficult to grasp what a bayesian predictive interval means. All in all, I recommend this MOOC, but you might find hard to fully grasp it.

Dec 24, 2017

Plus Points of the course -

It starts with a context of where and why bayesian statistics comes into play. Good real world examples and questions are posed to drive home this point at the start of the course.

Where it could have been more helpful -

1) Somewhere in between the course gets lost in math expressions and distributions drifting away from real world implications. This would be ok for someone looking for pure math/stats. However it would become less relevant for someone coming from data science/business side. More real world use cases could have been there. (2) Better guidance on which other streams of data science/business can have application of this knowledge would be helpful (3) More comprehensive set of resources (pdf ones) would be great

Feb 01, 2019

The topic of the course is very interesting and the subject warrants it. Yet, especially the coverage in the last week of the course appears to be shallow and too many concepts are pushed down as valid or true without a lot of theoretical justification. Besides, some of the interesting conclusions are part of the quizzes rather than an integral part of the lectures. I also think that a course like this should allow the students to receive more written material in the form of PDF files that would cover all the matters being explored. What is made available is fragmented and does not cover all the topics in an organic fashion. I believe the course could be improved substantially.

Jun 22, 2019

I don't have background in math and statistics, in the first week of the lecture i can catch up with the lesson, but coming into week 3 and 4 it's really hard to me to understand what's happening, since the lecture / videos only talking about the formulas and only taught us how to use the formula. Actually for person like me who want to know Bayesian Statistics application in the real world and also fundamentals of it it's quite not recommended to took this lecture, honestly. However in the general understanding this lecture quite can help me how Bayesian thinking works what is the connection between likelihood, prior, how to choose prior, etc.

Oct 11, 2016

Liked the course, but it was a little easy (took four days total to do the material for the whole course). Many questions were left unanswered (such as how dependent the credibility intervals are on the choice of prior distribution and the assumed distribution of the data), and it didn't touch on later topics that are interesting (MCMC sampling). Again, good beginning course, but I was looking for more in depth study.

Aug 15, 2019

-1 star: Some discussions and derivations do not have adequate context and background. I expected more thorough explanation on concepts and more advanced topics. There are also a few minor typos that confused me. It is only a helpful introductory level course on Bayesian without depth.

-1 star: quizzes are not challenging enough and they only require plugging in numbers into equation. Not a good way to study

Sep 05, 2017

Nice introduction to Bayesian concepts. Presentation sometimes focused on the details of the calculations and could gain from more perspective. Sections relating to Normal variables - variance unknown and Linear Regression could be more explicit. Useful overall as an introduction, but require to get additional external material to get to the bottom of it.

Jan 31, 2017

It was pretty intuitive and easy to follow the first couple of weeks, but then the assumed knowledge of beta and gamma distributions and their frequentist usage, stood in the way of me fully grasping the Bayesian part of it. In the end I just copied the examples from the lectures and passed the tests ... without really getting it.

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.