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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
2,913 个评分
758 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

Aug 31, 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.

JB

Oct 16, 2020

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

筛选依据:

701 - Bayesian Statistics: From Concept to Data Analysis 的 725 个评论(共 751 个)

创建者 Yuzhong W

Oct 3, 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.

创建者 Damel L

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...

创建者 Olexandr L

Jul 1, 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

创建者 Jesús R S

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.

创建者 Silvia Z

May 8, 2020

In general, the course is useful, but in half of videos the explanation focused mostly on formulas, and less on theory. I personally had difficulty in learning theory of Bayesian statistics.

创建者 Borja R S

Apr 25, 2020

The teachers are clearly experts in what they do, but sometimes I think it is that same expertise that makes them jump to conclusions too easily, making it difficult for beginners to follow.

创建者 Ran W

Jul 25, 2020

This course gives a very brief background on conjugate prior. However, the lectures on Bayesian linear regression is too superficial. I wish the lectures could have gone into more detail.

创建者 Carlos

Apr 8, 2020

Too much time spent on the beginning and too little on later more complicated concepts such as the posterior predictive. It felt as if that was just a side note in the extra readings.

创建者 Augusto S P

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.

创建者 Oliver B

Jun 1, 2020

Solid mathematical grounding, but would have benefited from more time spent on the history of Bayesian inference, when to use it, why it can be used etc..

创建者 Pranav H

Jul 1, 2018

The course could have given more information on tiny details which can confuse people during the exercises. But overall a good learning experience

创建者 Ángel L

Jul 4, 2021

It’s ok to have a theoretical basis about Bayesian Statistics, but I missed some practical cases using Python instead of R. I also missed PYMC3

创建者 Kathryn L

Jul 23, 2021

It's a nice introduction to the topic, but I often found the lectures to be imprecise or inconsistent, especially with respect to terminology.

创建者 Alessandra T

Jun 29, 2017

We still don't understand how Bayes differs to Frequentist... A worked example comparing the two at the end would have been nice.

创建者 Ken M

May 1, 2019

It would have been great if more graphs had been provided, for easier visualization of the e.g. distributions, or concepts.

创建者 roger

Jul 24, 2019

It would be better to add more explain about those equations and connect the math stuffs with the real world samples

创建者 Max H

Jul 14, 2019

It would be much better if there was a more sufficient introduction to the various distributions used in the course.

创建者 Victor D

Jul 9, 2019

Very informative as an introduction to concepts, but nowhere near the deep dive I'm now interested in taking.

创建者 Isra

May 4, 2020

Good course!!... Additional examples of real life explained and done in R or excel will make it great

创建者 Andres F P A

Jun 18, 2021

A lot of formulas and not that much interpretation. It is a good start in Bayesian concepts.

创建者 Binu M D

Sep 21, 2019

Too much theoretical than practical applications. No need to give both R and Excel videos.

创建者 A A

Nov 26, 2018

Would have liked more problem solving and real-world application examples.

创建者 YIHONG J

Jun 15, 2020

The workload is manageable however the homework is somewhat challenging.

创建者 Hassan A

May 11, 2020

Not well organized.

No sufficient materials, references, etc.

Very short.

创建者 sokunsatya s

May 31, 2018

Overall, it's Ok. but the explanation is too short and incomplete.