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Bayesian Analysis with Python

You're reading from   Bayesian Analysis with Python Unleash the power and flexibility of the Bayesian framework

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Product type Paperback
Published in Nov 2016
Last Updated in Feb 2025
Publisher Packt
ISBN-13 9781785883804
Length 282 pages
Edition 1st Edition
Languages
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Toc

Table of Contents (10) Chapters Close

Preface 1. Thinking Probabilistically - A Bayesian Inference Primer 2. Programming Probabilistically – A PyMC3 Primer FREE CHAPTER 3. Juggling with Multi-Parametric and Hierarchical Models 4. Understanding and Predicting Data with Linear Regression Models 5. Classifying Outcomes with Logistic Regression 6. Model Comparison 7. Mixture Models 8. Gaussian Processes Index

Comparing groups


One common task in statistical analysis is to compare groups; for example, we may be interested in how well a patient responds to some drug, the reduction of car accidents by the introduction of a new traffic regulation, or students' test responses under different teaching approaches, and so on. Sometimes this type of question is framed under the hypothesis-testing scenario, with the goal of declaring a result statistically significant. Relying only on statistical significance can be problematic for many reasons: on one hand, statistical significance is not necessarily practical significance; on the other, a really small effect can be declared significant just by collecting enough data. Also, the idea of statistical significance is connected to computing p-values. There is a long record of studies and essays showing that, more often than not, p-values are used and interpreted the wrong way, even for scientists who use statistics on a daily basis. Under the Bayesian framework...

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