Day 4: Bayesian Networks
========================

* Teachers: C. Herrmann

Schedule
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* Lecture: Friday 9.30-11.30
* Practical session + discussion: Friday 1pm-4pm

For those attending the seminar online, please use the `following link
<https://us02web.zoom.us/j/83531952511?pwd=eGhpTkNhVENMaU1yMElvSVlaMmJSdz09>`_

Please make sure to install RStudio on your laptop as well as the R library `bnlearn <https://www.bnlearn.com/>`_ with the command:

.. code-block:: R

   install.packages('bnlearn')
   install.packages('ggplot2')
   install.packages('reshape2')
   install.packages('Ckmeans.1d.dp')
   install.packages('corrplot')
   install.packages('igraph')


Slides
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:download:`Slides on bayesian networks (C. Herrmann) <../documents/2022-01-21_MasterSeminar_noSolution.pdf>`

Link to practical session
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`Practical session on BN reconsruction <bnTutorial2022_noOutput.html>`_

:download:`BN tutorial R markdown file <../documents/bnTutorial2022_noOutput.Rmd>`


References
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* Probabilistic Graphical Models D. Koller & N. Friedman (MIT Press) Causality J. Pearl (Cambridge University Press)
* Bayesian Networks in R R. Nagarajan, M. Scutari, S. Lèbre (Springer) 

Review papers

* A primer on learning in Bayesian Networks for Computational Biology, C.
Needham et al. (PLOS Comp.Biol. 2007)
* Inference in Bayesian Networks, C. Needham et al. (Nature Biotech. 2006) Learning Bayesian Networks in R, S.Bottcher & C Dethlefsen
*  https://www.bnlearn.com/