Volver a Bayesian Statistics: From Concept to Data Analysis

4.6

1,397 calificaciones

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370 revisiones

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....

por GS

•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.

por JH

•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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361 revisiones

por Brian Knight

•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.

por Ayush Thada

•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.

por Fabian Stephany

•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.

por Priyabrata Dash

•Feb 11, 2019

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

por Venkataraghavan Punnapakkam Krishnan

•Feb 11, 2019

Loved the theory & analytical part of the course.

por Łukasz Frydrych

•Feb 05, 2019

I really liked the course.

What I think could be nice improvement would be more nsightful notes. Which means, that after every video, there should be a separate sheet with all the formulas being described in more detail, so that you can refer to them any time during quizes.

por Sameen Notra

•Feb 05, 2019

Thanks it was nice learning from wonderfu instructors.

por Muhammad Ammar Jamshed

•Feb 04, 2019

very helpful for Statistic analysis

por Francesco Lacapra

•Feb 01, 2019

The topic of the course is very interesting and the subject warrants it. Yet, especially the coverage in the last week of the course appears to be shallow and too many concepts are pushed down as valid or true without a lot of theoretical justification. Besides, some of the interesting conclusions are part of the quizzes rather than an integral part of the lectures. I also think that a course like this should allow the students to receive more written material in the form of PDF files that would cover all the matters being explored. What is made available is fragmented and does not cover all the topics in an organic fashion. I believe the course could be improved substantially.

por Elguellab Ali

•Jan 29, 2019

Likely course and practical: it help us to understand some basic notion for bayesian inference. But Some concepts are less clear and I think need more development and explication (like effective sample size, Jeffreys prior). Great job over all.

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