Bayesian Networks

Learning Goals

The material presented here is aimed at providing the reader with:

  1. A solid grasp of Bayesian networks
    • Have an overview of Bayesian principles
    • Be able to formulate Bayesian networks
    • Know how to interpret outputs of Bayesian networks
  2. Ability to execute Bayesian network analyses
    • Know how to apply the bnlearn package

Contents

  • We follow the material noted further down under Group Material
  • We prepared ourselves by working through the material noted for each session in the Preparation Column in Proposed Timeline
  • At the start of each session, I quickly presented a summary of the preparation material.

Material

  • Book “Bayesian Networks With Examples in R” by Marco Scutari & Jean-Baptiste Denis; available here
  • Book “Bayesian Networks in R with Applications in Systems Biology” by Radhakrishnan Nagarajan, Marco Scutari & Sophie Lèbre; available here

Disclaimer

If you find any typos in my material, are unhappy with some of what or how I am presenting or simply unclear about thing, do not hesitate to contact me.

All the best, Erik

Bayes Bayesian Statistics Statistics Networks Biological Networks

Last updated on Jan 1, 2021

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