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Learner Reviews & Feedback for Bayesian Statistics by Duke University

3.8
stars
794 ratings

About the Course

This course describes Bayesian statistics, in which one's inferences about parameters or hypotheses are updated as evidence accumulates. You will learn to use Bayes’ rule to transform prior probabilities into posterior probabilities, and be introduced to the underlying theory and perspective of the Bayesian paradigm. The course will apply Bayesian methods to several practical problems, to show end-to-end Bayesian analyses that move from framing the question to building models to eliciting prior probabilities to implementing in R (free statistical software) the final posterior distribution. Additionally, the course will introduce credible regions, Bayesian comparisons of means and proportions, Bayesian regression and inference using multiple models, and discussion of Bayesian prediction. We assume learners in this course have background knowledge equivalent to what is covered in the earlier three courses in this specialization: "Introduction to Probability and Data," "Inferential Statistics," and "Linear Regression and Modeling."...

Top reviews

MR

Sep 20, 2017

Great course. Difficult to apprehend sometimes as the Frequentist paradigm is learned first but once you get it, it is really amazing to see the believe update in action with data.

GH

Apr 9, 2018

I like this course a lot. Explanations are clear and much of the (unnecessarily heavyweight) maths is glossed over. I particularly liked the sections on Bayesian model selection.

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251 - 255 of 255 Reviews for Bayesian Statistics

By Meta G

•

Sep 5, 2023

There is almost no one taking this course so getting the peer review can take forever

By Ashish C

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Aug 29, 2019

The quality of teaching was drastically down as compared to other courses.

By Jeffrey W

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Jun 2, 2018

Unclear information, too vague, incomplete presentation of ideas.

By Jose C G

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Dec 5, 2022

It is a pity that the course is for R

By Shubham J

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Sep 15, 2019

becomes too much confusing at times.