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Learning Probabilistic Graphical Models in R

You're reading from   Learning Probabilistic Graphical Models in R Familiarize yourself with probabilistic graphical models through real-world problems and illustrative code examples in R

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Product type Paperback
Published in Apr 2016
Publisher Packt
ISBN-13 9781784392055
Length 250 pages
Edition 1st Edition
Languages
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Toc

Index

A

  • ancestral sampling
    • about / Sampling from a distribution
  • arm package
    • about / More packages in R

B

  • basic sampling algorithms
    • about / Basic sampling algorithms
    • standard distributions / Standard distributions
  • Bayes' rule
    • conditional probability / Bayes' rule
    • formula, interpreting / Interpreting the Bayes' formula
    • example / A first example of Bayes' rule
  • Bayes' rule, in R
    • example / A first example of Bayes' rule in R
  • Bayesian Linear models
    • about / Bayesian linear models
    • over-fitting / Over-fitting a model
    • graphical model / Graphical model of a linear model
    • posterior distribution / Posterior distribution
    • implementation, in R / Implementation in R
    • stable implementation / A stable implementation
    • packages / More packages in R
    • packages, in R / More packages in R
  • Bayesian Naive Bayes
    • about / Bayesian Naive Bayes
  • Bayesian theory
    • references / Books on the Bayesian theory
  • ...
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