# 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
2,127 个评分
559 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.

## 401 - Bayesian Statistics: From Concept to Data Analysis 的 425 个评论（共 549 个）

Apr 17, 2017

I think that, besides lesson 11 and 12, everything was very well explained. I was a bit confused with lessons 11 and 12 since I am not new to econometrics. Perhaps I found it confusing the theory background related to the lessons themselves. Just my opinion, very good course.

Feb 05, 2019

I really liked the course.

What I think could be nice improvement would be more nsightful notes. Which means, that after every video, there should be a separate sheet with all the formulas being described in more detail, so that you can refer to them any time during quizes.

May 22, 2018

Overall the course is pretty good. They breakdown the concepts into clear and concise lectures. My only grip, is that the quizzes occur a little too frequently. They really interrupt the flow of the class. I would definitely prefer them spaced in 30-60 minute interval.

Oct 31, 2018

I found the need to search for most of the material needed to understand the lessons in other sources. Other than than it was a relatively easy class, which covers nearly the basics. This is not a tutorial on Data Analysis on R, although a short introduction is provided.

Nov 19, 2019

Course covers the concept in a very simple way. Examples and assignments are very good.

However some of the statements made throughout the lectures needs more explanation , the course did not dedicate any videos to get familiar with terminology related to probability.

Apr 17, 2020

It is a really interesting course. However, I think it should include more examples and meaningful ways to estimates some parameters. For example, how can I estimate alpha and beta for an Inverse-Gamma distribution in order to obtain a prior for the sigma-squared?

Jan 14, 2017

Pen hard to see against shirt. Was mildly irritating to wait for prof to write out stuff, maybe prewrite it?

Went too fast forward for me, would've liked complementary optional material, eg extra quizzes, to help understand and get used to the tougher parts.

Jul 09, 2017

Great introductions to Bayesian statistics and inference. Quiz is actually not easy just by passively viewing videos, so taking notes during lectures is strongly recommended. Do not be afraid the Honor quiz...they are not so different from the normal ones.

Jan 29, 2019

Likely course and practical: it help us to understand some basic notion for bayesian inference. But Some concepts are less clear and I think need more development and explication (like effective sample size, Jeffreys prior). Great job over all.

Mar 14, 2017

The lectures were good, but I hope more background materials can be released. Understanding the topics needs a relative solid mathematical background. Although having completed the course, I am still confused about some concepts in this course.

Sep 04, 2017

The course was excellent !...Giving a good overview of the basics needed to navigate through this topic. However, it would have been really great if some specific examples with respect to medicine and public health practice were incorporated

Mar 15, 2018

I found it to be a solid course. It has given me better grasp of the basics. I also found it a bit dry, and significant time spent on equations rather than high-level understanding. This is fine, as long as you know what you are in for!

Jan 07, 2020

Overall the class is great, especially the first two weeks' content is simple and well-explained. But from the week 3 to the week 4, the professor only writes many formula and doesn't provide enough examples to explain those formula.

Jun 17, 2019

Very high quality course. Could use some modifications (e.g. few more applied examples for regression using specific priors, MCMC etc.) and implementing some simple metaphors to introduce some topics before jumping into the maths.

May 04, 2019

Very good course for beginning bayesian inference. The syllabus is easy to follow, but I also think one could benefit even more by complementing the lectures with other sources (books or other youtube explanation)

Apr 10, 2017

Short course that's actually mostly theoretical with a bit of R/Excel analysis. This fitted my needs perfectly. My only suggestion is that they should have released more comprehensive notes for the lectures.

Dec 12, 2019

The course content is great and provides a good introduction to bayesian statistics. The assignments could be a little more challenging as a lot of the questions require just plugging numbers into formulae.

Jan 15, 2018

I could grab the concept of Bayesian statistics but did not find the course fully self-contained. I had to look elsewhere to fully understand details. More complete supplementary material could help a lot.

Oct 10, 2018

Good use of R but maybe use the actual coefficient from the equations themselves rather than picking numbers pre-selected which may confuse.

Unable to look at discussion forum without posting myself.

Feb 19, 2020

the notes for the lectures are missing.

In my opinion the notes, which includes the video materials could be very useful.

the course was good. I learnt some new concepts in bayesian thinking.

Sep 25, 2019

Very clear and informative. Would like a more extensive and combined reference material (PDF, so less need to lookup e.g. definitions of effective sample size for various distributions).

Dec 09, 2019

It was a good course for me to get familiar with the new perspective on statistics. Thank you!

Maybe, some extended practice exercise at the end of the course would make it even better)

Nov 26, 2016

A good course but neither notes nor lectures were not in much details. But still it was worth my time. I strongly recommend it if you want a subtle introduction to Bayesian Statistics.

Jul 07, 2017

Great course (and teacher). Assumes some basic highschool level for math. With experience in frequentist statistics, but not all the distributions this course was "easy" to follow.