This class will cover use cases of the Bayesian methods in the medical domain. First part of the class is based on article: “Local computations with probabilities on graphical structures and their application to expert systems” by Lauritzen, Steffen L. and David J. Spiegelhalter. Second part is inspired by “An intercausal cancellation model for bayesian-network engineering. International Journal of Approximate Reasoning” by S.P. Woudenberg, L. C. van der Gaag, and C. M. Rademaker.

In this section we will follow a simplified use case of the medical diagnosis, as defined in the following quote from the article.

First task of the “knowledge engineer” is to find a structure of Bayesion network which fits the story. There exist automatic tools to learn the structure from examples, but in this case the structure should be clear enough to create the network by hand.

1. Draw (on paper?) a Bayesian network describing the story from the previous section.
2. Write the corresponding ProbLog program:
   1. there is no need for the first order logic here
   2. use arbitrary probabilities