Implementing Bayesian Network Induction


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Suitable for:
Student, Bachelor or Master project

Description:

Bayesian Networks and other graphical models for probabilistic reasoning are usually intuitive to use since their graphical nature allows easier interpretation. Probabilistic reasoning in general can be used for a wide range of scenarios and humans are argued to process information more or less in a probabilistic Bayesian way (subject to approximations of course). Our own inference implementation PRIMO (https://gitlab.ub.uni-bielefeld.de/scs/PRIMO) already allows the use of different kinds of networks as well as inference methods, however it does not support the induction or learning of models from data. If a structure is already known, learning the parameters from data is fairly sim- ple and straightforward. Learning the structure however, can be tricky and there exist many different methods. Depending on the scope (BA, MA or project), students should research and select appropri- ate methods that could be used in the existing codebase or adapt the existing codebase accordingly.

Recommended skills:

  • decent knowledge of Bayesian Networks and probabilistic inference
  • proficient knowledge in probability theory

Contact:
Jan Pöppel