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.
Oct 17, 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
创建者 Xiaoyang G•
Jul 07, 2016
This course is a very good introductory of bayesian statistics. But it better that you have known the basic statistics inference.
创建者 Humberto R C•
Nov 06, 2017
A clear and compact introduction. Quizzes and exercises are relevant. I got acces to grades and feedback in the audit one I took.
创建者 Raj s•
Feb 09, 2017
Learned something new :). Lecture were excellent, but, I need time to digest and hope I will get opportunity to use it in future.
创建者 Tetsuhiko O•
Jan 20, 2018
I studied basic theory from these lectures. I will try again and again until I understand Baysian Statistics concept completely.
创建者 Jose M R F•
Jul 14, 2019
Very well explained. Lectures are given in a very nice way as the professor writes. Exercises and quizzes are very well done.
创建者 Zhirui W•
Sep 26, 2017
Become very clear about all the formula and derivation of Bayesian Statistics after taking this course. Strongly recommended.
创建者 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
创建者 Leon W•
Aug 05, 2018
The video content is not too much. However, students can learn and practise a lot from supplementary materials and quizzes.
创建者 Salaheldin G•
Dec 26, 2017
Very useful crash course in Bayesian Statistics. It requires some basic knowledge in statistics and probability as stated.
创建者 Miles D R•
Aug 15, 2019
This course was dense, concise, and yet easy to follow for individuals that are fairly comfortable with basic statistics.
创建者 Francisco J S G•
Aug 26, 2018
A really hard course but useful for those who want to know more about statistics and how it is related to Bayes' theorem.
创建者 Álvaro C Q A•
Mar 28, 2018
It's a good introductory course to Bayesian statistics, a second part with Gibbs Sampling, Markov and MCMC would be nice.
May 17, 2018
The teacher is excellent and charming and the course is also easy to follow. However, with more exercise will be better!
创建者 Georgios P•
Feb 24, 2017
Very good introduction to baysian concepts and very helpful in understanding the difference with frequentist statistics.
创建者 Shakir B•
Aug 03, 2020
Initially I was a bit put off. But what a compilation of well thought set of lectures and quizzes! Thoroughly enjoyed.
创建者 Бызов А•
May 27, 2018
Marvellous course! Thank you very much! I would really appreciate, if you'll create an advanced version of this course
创建者 Flavio P•
Aug 10, 2017
Very interesting. It can help taking notes during the course... to avoid going again through it and take them ex-post.
Jul 06, 2017
Excellent course, although it would have been nice to get more content on uninformative priors and Fisher information.
Sep 24, 2016
This course are excellent and Thanks for Prof for offering the course. I've learned a lot from the course. Thank you.
Mar 10, 2017
I have learned a lot from this course. As there is not course like this one in my univeristy, I really appreiate it.
创建者 Zach K•
Aug 04, 2020
I learned a ton about statistics and probability distributions. It was great prep for my machine learning classes.
创建者 Xilu W•
Nov 20, 2016
I'm a graduate student in mechanical engineering. Thanks for the open course, it is really convenient and helpful!
创建者 Artur A B•
Aug 21, 2019
Very useful course, described a basic understanding behind Bayesian theory and sequential updating of posteriors.
创建者 Harsh V D•
Aug 06, 2017
A very well designed and productive course for anyone looking to brush up his/her concepts on Bayesian Statistics
创建者 Andrew N•
Oct 22, 2016
wrote in my comment.
The course is extremely well presented and the difficulty level of the excercises is perfect.