返回到 Bayesian Statistics: From Concept to Data Analysis

4.6

1,813 个评分

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468 个审阅

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....

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.

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.

筛选依据：

创建者 Xiao X

•May 27, 2018

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.

创建者 Larry L E

•Oct 05, 2016

I enjoyed the course. My background is mathematics, but not specifically statistics, though I do have a basic understanding of elementary frequentist statistics. My goal was to understand the fundamentals and uses of Bayesian statistics, having attempted that via a couple of textbooks without much success; this time, I got it!

I do have some reservations about the course. Herbie Lee spent a huge amount of time deriving formulas and methods - a few gaps (either hand waving or 'leave it to the student to finish') would have been helpful, I think. This would leave more time for examples and applications. But the course was well worth my time and effort.

创建者 Yuanruo L

•Nov 05, 2016

Good and simple introduction for Bayesian statistics.

创建者 Xu Z

•Apr 07, 2017

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).

创建者 Valentina D M

•Mar 29, 2018

Need more material on R.

创建者 Jurriaan N

•Dec 17, 2016

This course provides the student a profound understanding of the statistics behind the bayesian approach. Also, it gives some intuition for the difference between the frequentist and the bayesian approach, although that part could have been more explicit in my opinion. It would be very helpful to have more examples on the differences in using freq vs bayesian approach, the gains from using bayesian approach, examples of where the freq approach is limiting / misleading in its 'objectiveness'. More 'real life' examples instead of coin flipping examples - although easy to follow - would be very helpful as well, maybe in a consecutive course with applied bayesian statistics?

创建者 Somnath C

•Dec 03, 2016

I wish the last week were more explanatory. Although overall now I do have an idea. It's a good course. :-)

创建者 Rohit J V

•Feb 04, 2018

As a graduate student pursuing Machine Learning, this was a great course for me to get introduced to Bayesian Models.

创建者 Katsu

•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.

创建者 Florian M

•Mar 02, 2018

Herbert Lee is great at explaining the mathematics behind Bayesian statistics. However, I think the course can improve greatly by also focusing more on context and the intuition behind the mathematics. I often found that I was able to pass all quizzes, while I did not 100% understand why I was doing what I was doing.

创建者 Jakob W

•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!

创建者 xuening

•Jan 26, 2017

from week 3, the learning curve become steep

创建者 Víthor R F

•Jan 12, 2018

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.

创建者 Tuhin S

•Sep 01, 2017

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

创建者 Hu S

•May 08, 2017

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

创建者 Pranesh K

•Mar 10, 2017

The course is excellent to learn all the basic stuff needed to master the technique of Bayesian Data Analysis.

创建者 Simon N

•Sep 09, 2017

This is a nice, bite-sized introduction to Bayesian inference. Helpful lecture notes are provided, alongside introductions to practical computations using R and Excel.

创建者 abhisingh03

•Jan 15, 2017

This course has given me some good new insights into perceiving data and has got me started nicely I am very great full.

创建者 Jerry S

•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.

创建者 h

•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.

创建者 재환 맹

•May 23, 2018

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.

创建者 David N

•Jul 30, 2017

It was a really well taught class and I enjoyed watching it. Unfortunately I seem to lack some basic understanding, since I am not a statistician. Therefore I had problems following the course and had to do quite a bit of research to do on my own to get long. Still, I managed to get 100% correct on all quizzes and all honours quizzes. So it seems, that if you put in enough effort, you can get 100% on the course without understanding many things. This is not to say, that this course is easy, it took a LOT of effort, but it was possible. I will now investigate further to get all of the basics. Maybe I will come back and take this course as a refresher. Other than that I can whole-heartedly recommend this course. The presented material is very well organized and and presented and Professor Lee is a really good teacher.

创建者 Óscar S F

•Sep 19, 2017

Very straight-to-the-point course. Very dense, though, for a newbe in bayesian terms and concepts. But I definitely suggest it to undertand priors and posterior concepts. Thanks!

创建者 spencer r

•Oct 01, 2016

There are several things in the course that were able to clear up my understanding. The course instructor responds to more questions than I would have expected as well. The course uses a lot of mathematical notation and it helps to take some time with it but once you get the idea of conjugate priors down you can quickly employ them in your own problems. The course covers conjugate priors for several different likelihoods including the normal distribution and the binomial distribution. Although the derivation of the conjugate priors looks daunting as it is written down, the usage of the priors make Bayesian statistics much easier.

This course uses R and Excel but is not a course in either. Most of the computations that are performed for the quizzes are pretty simple and require little skill in R.

I am glad that I have taken the course and would take another if provided by this instructor. I plan to reference the materials provided in the future whenever I need a refresher.

创建者 Sameer G

•Nov 04, 2017

Hi , this course opened a door for me in Data analysis. Very intuitive & must course for any person exploring data science.