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4.6

1,695 Bewertungen

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440 Bewertungen

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.

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von Scott S

•Oct 28, 2018

This course gives an introduction to the theoretical basics of Bayesian statistics. Before taking this class, I had a very confused view of the whole Frequentist vs Bayesian "debate". I understand now that Bayesian statistics is really about attaching uncertainties to beliefs and producing a clear definition of this uncertainty (especially through the notion of credible intervals).

The course really focusses on theory. I recommend knowing a bit of basic stats concepts before taking the class, such as Bayes' Theorem, basic discrete and continuous distributions, and confidence intervals. If you are not experienced with these, be aware that you will likely need to read-up on them throughout the course. R is used, but the usage is so simple that you should not shy away due to a lack of R experience.

I really have no complaints about the course. After completing it, you should understand the differences between Bayesian and Frequentist approaches. You will also understand a lot of terminology that gets thrown around in data science these days (priors, posteriors, credible intervals).

von Jonathan

•Jul 02, 2018

So, I really wanted to LOVE this class, but instead I found that I merely liked it, and want to use this review as a way to explain why. WHAT I LIKE ABOUT THE CLASS: The material is sufficient for the topic at hand, and is structured in an appropriate way. If you work through everything you'll have a decent grasp of exactly what the class is meant to be about. It's also pretty well paced. WHAT I DIDN'T LIKE ABOUT THE CLASS: Dr. Lee usually rushes through or skips discussions what concepts mean before formalizing them mathematically. As a result it's very easy to make progress through the class without a good feeling that you actually "get" what Bayesian statistics is really about. Too many of these videos are him chopping wood through the mathematical jingo, when the material DESPERATELY needed a 3-5 minute introductory video about what concepts actually mean or how to think about them. I remember telling my girlfriend during the middle of the class that I found it frustrating because I was progressing through it quickly, and getting the quizzes right, but lacked a good intuition for how to think about Bayesian statistics. So Dr. Lee......work on those presentation skills! Think deeply about how to communicate the essentials of the concepts in each lesson, and THEN start pounding away on the whiteboard!

von Megan G

•Jul 26, 2017

I felt like I just did a lot of calculations. The course was better in the beginning, as I felt the professor actually explained what and why were were doing what we were doing. By the middle of the course, however, I felt that the professor just jotted down equations and went really quickly. I don't actually understand why I was doing the calculations that I was doing.

von DM C

•Jun 11, 2018

I don't find that the lectures do a good job of relating the material to real world usage. To much focus on equations and too little on the why.

von Timo K

•Mar 13, 2019

Very good overview to the area. Efficient and clear lectures - emphasis on the quizzes that required just a proper amount of focus and time from my personal point of view.

von Yifei H

•Dec 22, 2018

Very concise and helpful for an intro to Bayesian statistics. Good level of difficulty to encourage learning. This well prepares further study of more advanced topics such as MCMC and more.

von Asael B I

•Feb 28, 2019

a really good course!

though sometimes the questions in quizes aren't clear enough,or not explaind else where,and sometime you could miss the big picture.

could also be good if you could add some python scripts,and maybe more reading material about the topics.

von Iryna

•Feb 16, 2017

If you already know everything about the topic and just forgot some little things or you are very strong in calculus, this may be a nice refresher. Otherwise, not very useful. Really dense and little explanation. I liked the Youtube MIT course on Probability (it includes Bayesian Statistics) much more, since it has good explanation of the concepts.

von Nathan A

•Dec 19, 2018

A well-rounded introductory course in Bayesian Statistics.

von raymonsahib

•Dec 24, 2018

Great introduction for beginners

von Valeth

•Dec 13, 2018

The bite-sized arrangement of individual videos are very conducive for learning and self internalisation.

von Eduardo M

•Jan 04, 2019

Very good material! The Prof explains very easily the contents of the course. Great course! I recommend. E. Martins, Brazil

von Sameen N

•Feb 05, 2019

Thanks it was nice learning from wonderfu instructors.

von Muhammad A J

•Feb 04, 2019

very helpful for Statistic analysis

von Niklas J

•Nov 21, 2018

Good and clear course. I was impressed by the quality of the material.

von Robert T

•Nov 07, 2018

Great course for beginners as well as those that need a refresher on the basics!

von Susenjit G

•Nov 23, 2018

Very Useful Course

von Michael W

•Jan 16, 2019

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

von Georgy M

•Jan 10, 2019

I found the course very well made and beautifully presented. The material is systematic, the more advanced topics based on the previously learned information without gaps and any need to study additional sources. The examples and the tests provide additional insights. Thank you, prof. Herbert Lee, for this great course!

Was able to do the course with Python instead of R, though it got a bit complicated on the last topic (regression).

von Yiwen

•Jan 25, 2019

The course if very informative but some previous knowledge and understanding of basic statistics (e.g., distributions) is recommended to fully grasp the concepts delivered through the course.

von Brian K

•Feb 19, 2019

Great introduction to Bayesian statistics. Very helpful for me, especially for understanding some of the times when priors might be useful, and how they can aid me.

von Priyabrata D

•Feb 11, 2019

best course for the beginners who want to get started on bayesian inference

von Fabian S

•Feb 17, 2019

A great introduction to Bayesian Statistics. For some of the Quiz questions, it would have been helpful to get an error warning in case one might had accidentally used a comma instead of a dot notation.

von Ayush T

•Feb 18, 2019

It's really a good course for Bayesian Statistics. Exercises are designed in such a way that they can't be passed if you've not understood the topic completely. The workload is manageable and the course content is really well organized.