Chevron Left
返回到 Bayesian Statistics: From Concept to Data Analysis

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
2,695 個評分
704 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....

熱門審閱

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

篩選依據:

426 - Bayesian Statistics: From Concept to Data Analysis 的 450 個評論(共 690 個)

創建者 Ankur S

2020年1月17日

Very good course

創建者 Justin C

2018年8月25日

Excellent Course

創建者 Gaurav a

2017年12月26日

Very encouraging

創建者 Martin K

2017年2月23日

Best course yet!

創建者 Andrei M S

2020年9月23日

Learned a lot.

創建者 Jakob R

2017年5月10日

Great course!

創建者 조휘용

2020年6月29日

good course!

創建者 Efren S

2017年12月18日

Great stuff!

創建者 FNU R M

2019年8月15日

Nice Course

創建者 Binghao L

2019年4月11日

nice course

創建者 Joshua M

2017年10月10日

Good course

創建者 Zito R

2018年2月27日

Excellent!

創建者 Rigoberto J M A

2017年11月6日

Excellent.

創建者 Vinicius P d A

2017年4月19日

Very good!

創建者 Hortensia M

2021年4月12日

excelent!

創建者 FERDINANTOS K

2020年11月14日

THANK YOU

創建者 Benjamin S K

2020年9月12日

recommend

創建者 How

2018年9月28日

Completed

創建者 Jinxiao Z

2018年6月21日

excellent

創建者 Shashi R

2016年9月15日

Awesome.

創建者 Xinyi J

2019年4月8日

Great!

創建者 Anna B R

2017年12月17日

Great!

創建者 Wai Y L

2017年6月10日

Good!

創建者 Benjamin A A

2018年5月21日

j

創建者 Artem B

2018年2月7日

This is a great course and I have learned a lot. The teacher is extremely knowledgeable and formulates things very clearly. However, this is really a math course. For me it was hard to stay motivated because the language of the course is mathematics, the teacher juggles with the concepts that my mind was still trying to process and absorb. I was able to finish all exercises, including the honors ones, but when I finished the week 3, I had to redo it completely again and buy a book on Bayesian statistics by John Kruschke which helped me immensely to rethink the basic concepts again. This course could be excellent if it included more reiterations of concepts, was explained in more general language, the pace was slower and most importantly included more practical applications. The typical statistical examples of coin flipping are fun, but too abstract. In the end, I want to know how I can apply Bayesian statistics. A lot of knowledge of mathematics was assumed and I had to look up a lot of concepts myself. The derivations sometimes also went too quick and supplementary materials were quite dense. I think this course is a perfect refresher course for someone who has mathematical background and has taken a Bayesian statistics course some time ago. But for the beginner with some mathematical background (I am familiar with the frequentist statistics, machine learning, calculus) it was too much of a challenge. If it were not a Coursera course, where I can rewind endlessly and work at my own pace, but a regular university course, there will be p=.9 that I would drop out, while my prior for dropping out would be p=.05