Bayesian Networks Study Group

Disclaimer

This study group is currently active. We are now coordinating through Slack to get our fix of all things Bayes. Our Slack Workspace is open to anyone who wants to join us. To do so, simply let me know by contacting me and shortly introduce yourself.

Motivation

Building on the success of the Bayes Study Group I ran two years ago, I have decided to create another study group this time focusing on explicit inference and analysis of Bayesian Networks which I believe are a neat tool for hypothesis testing.

Conduct & Logistics

Below, you will find a few key facts outlining how this Bayesian Network Study Group sessions are run. You will find that what we do is quite a lot (I would like to think it quite exhaustive, actually).

That being said, I realise that what I propose is quite a big-time commitment – both in per-week hours and for how long this study group is supposed to run.

Meetings:

  • Scheduling:
    • Tuesday 1500-1700 CEST (sessions end earlier if we are done earlier, of course)
    • Weekly (see Proposed Timeline)
  • Location: Zoom @ https://aarhusuniversity.zoom.us/j/65455334420 (meetings will be recorded to make sure everyone can stay up-to-date even if they miss one or more sessions or are unable to join on-time, or stay for the entire duration).

Contents

  • We follow the material noted further down under Group Material
  • We prepare ourselves by working through the material noted for each session in the Preparation Column in Proposed Timeline
  • At the start of each session, either me or a volunteer quickly presents a summary of the preparation material. This does not mean a presenter should have mastered the material at all – simply be able to provide a red-thread through the contents and get a discussion going. We then go into questions/challenges concerning the material, practical examples, and personal implementations.

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]https://link.springer.com/book/10.1007/978-1-4614-6446-4)
Erik Kusch
Erik Kusch
PhD Student

In my research, I focus on statistical approaches to understanding complex processes and patterns in biology using a variety of data banks.

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