Voltar para Bayesian Statistics: From Concept to Data Analysis

4.6

1,652 classificações

•

435 avaliações

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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por Michael B R

•Sep 30, 2017

Great course for an intro to the Bayesian statistical approach.

por Sergio D H

•Sep 21, 2017

Great introductory course on Bayesian data analysis. The course is self-contained and the videos make a great job explaining the concepts of each lesson. I truly appreciated the practical approach either with R or Excel.

por Sudhir K P

•Nov 16, 2017

This is first time exposure to bayesian statistics and I must say it has given me a different perspective to analyzing data especially when dealing with unpredictable data or unknown data.

por Deepak K

•Sep 17, 2017

Very well structured course. Problems and quiz are real life problems, and it's challenging and rewarding to solve them. Thanks to Prof. Lee for delivering an awesome course.

por Daniel J R V

•May 15, 2017

Great course. Delivers what promises: Bayesian Statistics: From Concept to Data Analysis.

por Adam B

•Jun 05, 2017

Great course as an introduction to Bayesian Statistics. Interesting material and the pace was very reasonable.

por Oaní d S d C

•Apr 22, 2018

Amazing. Simple, fast, dense, very well taught. I loved the professor, his commentaries and way to explain the contents. Thought the exercises were OK, maybe simpler than I taught but the comments in them helped me a lot to understand the topics. 10/10, a new and better way to teach! Very useful.

por Luis R P L

•Apr 24, 2017

Very good explain nd excelent material

por KK

•Dec 01, 2016

Although I only spent less than one week to finish the course, I think it is quite valuable if you are interested in statistical inference and want to learn more specific in Beyesian Statistics. High Recommended.

por Jeff N

•Mar 30, 2017

As a long time frequentist, I occasionally run into problems that are very awkward to fit into the frequentist paradigm. I was aware at a high level that the Bayesian approach could be applied more naturally. Unfortunately, I was unable to "get it" simply be reading a book on the subject. This course made it very approachable. Professor Lee showed us the difficult math (tough integrals) behind it and how we can apply the results of that math in Excel or R

por Anna B R

•Dec 17, 2017

Great!

por Mohan N

•Mar 03, 2018

Alot of information, concise and clarity is awesome. Would recommend this course to anyone. And I did too. Great, professor. My only suggestion is to speak a little slower.

por Zhu L

•Nov 26, 2017

A very well-organized course. Not a hard one, but one with sufficient quizzes to make sure you understand every concept by solving problems.

Another thing I like about this course, is that I had to actively write a lot of codes in Python and Matlab when doing the exercises(due to my familiarity with these two), although the course teaches a little bit R and Excel programming. This is a very effective way of teaching.

por robert s

•Dec 22, 2016

Instructor responds quickly to problems. Good job.

por Orfeas K

•Mar 02, 2018

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

por 杨铭宇

•Jul 05, 2017

A good course for introducing to Beysian pobability. Easy to understand and very practical.

por Ron A

•Apr 12, 2017

Excellent course. Professor Lee did a first-rate job of giving the intuition for Bayesian methods and building the foundations for further study of the topic. The course is short and to the point, but that is a feature and not a bug. It will prepare you to take further courses in Bayesian statistics or to study the topic on your own. Highly recommended.

por Gaurav a

•Dec 26, 2017

Very encouraging

por Thadeu F

•Jul 05, 2017

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

por Mohini R

•Aug 18, 2016

I enjoyed the course.

por Max L d M

•Jul 24, 2017

Very good course!

por Julio C L J

•Jun 12, 2018

It was an excellent course. It´s a very good introduction to the Bayesian approach to inference.

por Cooper O

•Jun 28, 2017

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

por Joshua M

•Oct 11, 2017

Good course

por Felipe M

•Oct 23, 2017

Very practical and insightful course. Strongly recommend it.

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