Bayesian networks are probabilistic graphical models used for understanding how different variables interact with each other. They are built by exploiting the conditional dependencies of each variable using Bayesian theory. For example, let's assume that we have three variables: sleep quality, diet quality, and work performance. For the sake of simplicity, let's also assume that each variable can only take two values: high and low. In our usual regression or classification framework, we would model one of these variables in terms of all the rest. Of course, we would need to take care to choose a dependent variable that is caused by the covariates in some way (in order to make an inference in a regression context, causality needs to flow from the covariates to the dependent variable). BNs operate differently, and in this case, we...
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You're reading from R Statistics Cookbook
Product typeBook
Published inMar 2019
Reading LevelExpert
PublisherPackt
ISBN-139781789802566
Edition1st Edition
Languages
Tools
Concepts
Author (1)
Francisco Juretig
Francisco Juretig
Francisco Juretig has worked for over a decade in a variety of industries such as retail, gambling and finance deploying data-science solutions. He has written several R packages, and is a frequent contributor to the open source community.
Read more about Francisco Juretig
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R Statistics CookbookPublished in: Mar 2019Publisher: PacktISBN-13: 9781789802566
© 2019 Packt Publishing Limited All Rights Reserved
Author (1)
Francisco Juretig
Francisco Juretig has worked for over a decade in a variety of industries such as retail, gambling and finance deploying data-science solutions. He has written several R packages, and is a frequent contributor to the open source community.
Read more about Francisco Juretig