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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
2,675 個評分
697 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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176 - Bayesian Statistics: From Concept to Data Analysis 的 200 個評論(共 685 個)

創建者 Fabian S

2018年1月18日

A great introduction to Bayesian Statistics for everyone who has some basic knowledge of calculus and is familiar with the fundamentals of probability theory.

創建者 Antoine N

2017年8月21日

Great introduction to the Bayesian framework! The exercises are relevant and I look forward to the second part (Bayesian Statistics: Techniques and Models).

創建者 Isaac D

2017年1月20日

A step by step course, designed to pay attention all the time with tons of practical examples and very clear explanations, I would definitely recommend it!!

創建者 jl b

2020年6月11日

Herbert is clear, gives great examples, and is easy to follow. The question prompts are helpful, and the quizzes thoughtful and challenging. Great course.

創建者 Naveen M N S

2017年9月21日

Very good course for fundamentals of Bayesian statistics. Made me understand Monte Hall problem, conditional probability, etc. in a totally different way.

創建者 Pawel R

2016年10月3日

The course creates great foundations for digging deeper into more complex concepts and trying to run some Bayesian statistics on simple real life problems

創建者 Mohan R

2019年12月1日

A mathematics course I really enjoyed because the instructor was actually teaching the material as best as one could without meeting the students. Great.

創建者 Laure N

2018年3月6日

Thank you very much for sharing your knowledge with the public. Now I am no more afraid to face the book 'Bayesian Data Analysis' by A. Gelman et al.

創建者 Allan V d C Q

2020年5月7日

I really enjoyed this course. Dr. Lee is a really good instructor. The materials and tests are good as well and will help you during the journey.

創建者 Thadeu F

2017年7月5日

Great course. Intermediate to advanced level (at least for me). You must have good foundation in probability. If so, you will learn a lot. Thanks

創建者 Simiao R

2020年7月20日

Good course about bayesian! I finally understand the relationship between frequentist idea and Bayesian approach and Beta gamma distributions

創建者 Eben E

2020年4月12日

This was a were educational course. I had trouble understanding R programming but with this topic, most of the programs became more clear to me.

創建者 Cooper O

2017年6月27日

A Fantastic course. Detailed learning materials, Lots of opportunities to test your knowledge, and difficult enough to make you learn something!

創建者 Nitin K

2017年6月1日

I loved everything about this course. It reminded me of my time in school. Papers and pencils. I look forward to attending the follow up course.

創建者 Tiannan S

2020年7月6日

As a computer science student, I feel Bayesian approach is much more intuitive and more computationally friendly than the frequentist paradigm.

創建者 Fernando D L

2019年3月11日

It's a good course to know the principal concepts of Bayesian statistics. Also, the course has excellent examples to understand thew concepts.

創建者 Orfeas K

2018年3月2日

I really appreciated the content, and the way it was taught by Prof. Lee. His explanations were intuitive, without loss of mathematical rigour.

創建者 Giovanni G

2020年7月29日

Consistent and mathematically dense. If you want to go through every passage this course gives you solid understanding of Bayesian statistics.

創建者 RIcardo G M

2019年12月15日

Very good course. Concepts are very well explained, and quizzes are really helpful to apply and further

understand the explanations provided.

創建者 Kuntal B

2019年11月13日

Thanks, Coursera. This is a good course. It would be helpful if we get any proper class notes on Jeffrey's prior and Multivariate regression.

創建者 Artem B

2019年7月3日

Great course with a lot of simple, but illustrative exercises. It may be useful to have some basic prior knowledge of econometrics/statistics

創建者 Michael W

2019年1月16日

Great introductory course. It was challenging but doable for someone who has not take college level mathematics or statistics in a few years.

創建者 Robert K M

2018年2月11日

Invaluable. Excellent quizzes. A few terms could have been better defined, and a few more examples wouldn't hurt, but overall excellent.

創建者 Damian C

2016年11月10日

Very well presented course. Interesting and intuitive introduction into the fascinating Bayesian world.

Many thanks and congratulations!!!

創建者 Ariel A

2017年10月12日

Great course, it has the right proportion of theory and practice. It's a great start for anyone who wants to dive into Bayesian Analysis.