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學生對 加州大学圣克鲁兹分校 提供的 Bayesian Statistics: From Concept to Data Analysis 的評價和反饋

4.6
2,604 個評分
682 條評論

課程概述

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....

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GS
2017年8月31日

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.

JB
2020年10月16日

An excellent course with some good hands on exercises in both R and excel. Not for the faint of heart mathematically speaking, assumes a competent understanding of statistics and probability going in

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526 - Bayesian Statistics: From Concept to Data Analysis 的 550 個評論(共 670 個)

創建者 Przemysław M J

2017年7月10日

Nice explanations of the theory, however there could be a bit more written materials and the pace could be slightly slower, especially regarding the last chapters.

創建者 Ruben S

2019年3月3日

I took this course both to refresh my basic understanding of statistics as well as to learn what Bayesian Statistics was about. This course was good fit for this.

創建者 Anuj K S

2020年10月24日

It is a good basic course with lot of math. However, personally I found it little difficulty but yo can make sense of everything if yo watch the video 2-3 times

創建者 mingzhuo

2018年8月25日

Though Bayesian statistics is not easy, and quite complex when dealing with prior and posterior. This class provides a good overview the the Bayesian statistics.

創建者 Fatemeh F

2020年10月7日

The course was good, but the required time was much different from the one written in the course description. It has a lot of quizzes, which take a lot of time.

創建者 재환 맹

2018年5月22日

Intuitive course, but somewhat fast which leads students to pause and contemplate on what the lecturer had to say. Good start to get to know Baysian Statistics.

創建者 Xiao X

2018年5月27日

The explanation is very in details. It would be better to have more mathematical derivation in the linear regression part besides the demonstation of using R.

創建者 Hu S

2017年5月8日

Overall a good course about Bayesian inference. Only suggestion would be to spend a bit more time explaining the interpretation behind the calculated numbers.

創建者 Arthur M

2018年3月30日

Very good introduction to bayesian statistics, but I would have liked a bit more written material to complement the videos, who were rather short and fast.

創建者 Víthor R F

2018年1月12日

It is interesting learning the mathematics behind the analysis, but it could have been more complete, with a little less theory and more data analysis.

創建者 xu w

2017年9月2日

this is a very good introductory course on Bayesian Statistics. Thought you will not learn deep from this course, it will give you a good big picture.

創建者 Tuhin S

2017年9月1日

Great course with easy to understand examples. One can explore deeper into the world of Bayesian statistics after completing this preliminary course.

創建者 Bae,Bongsung

2020年9月8日

All the weeks were great, but the week 4 seems to be in complete and lack of explanations. Some refinement on the week 4 materials would be great.

創建者 Yalong L

2019年10月10日

The first question in Week 4 Honor Quiz, the coefficient for intercept, I got 138 which you show incorrect, would like to know the correct answer.

創建者 Taylor J W

2018年1月1日

Very good intro to Bayesian statistics. I only rate 4/5 because the second week was disproportionately more difficult than the other three weeks.

創建者 Lucas J

2017年8月27日

I've always found stats kind of boring but, the material covered in this course is invaluable. Dr. Lee presents everything clearly and concisely.

創建者 Philippe B

2020年12月28日

Great! Clear, systematic... but: requires a good basic knowledge of mathematics and lacks practical examples to illustrate the models presented

創建者 Việt P H

2020年6月28日

A nice course. I gave me a fundamental knowledge about Bayesian Statistics. The lectures are sometime a bit confusing but overall, it's great.

創建者 Sydney W

2020年8月18日

more examples of solving problems would have help. or having direct references to sources that explain the technical aspects of the material.

創建者 Seth T

2020年12月11日

The course could use slightly more explanation of how Bayesian statistics is applied to real world problems (vs. frequentist application).

創建者 Massimo G

2019年11月17日

Very good method and quality of teaching, I'd recommend more solved and commented exercises for each topic exposed, before each week test.

創建者 Xu Z

2017年4月7日

Very concise and easy to follow to the end. The linear regression part could be more clear (i.e., with a lecture on the background).

創建者 Alex C

2020年2月17日

The last section, normal data, which is very important, could have been instructed in a slower, less hasty way with more details.

創建者 Björn A

2020年6月21日

Great course to get acquainted with Bayesian statistics and inference. Just wished seeing a bit more of mathematical background.

創建者 Devid

2018年11月28日

Need more information about linear regression, given material is not enough to understand topic and effectively find solution.