The aim of this set of exercises is to build up experience in developing Bayesian networks for realistic clinical problems. The exercises included in this assignment learn you something about the relationship between consulting Bayesian networks, using tools such as SamIam or Genie (See below), and problem solving. We start by describing two software tools for building Bayesian networks by hand...

This paper presents new results on the complexity of graph-theoretical models that represent probabilities (Bayesian networks) and that represent interval and set valued probabilities (credal networks). We define a new class of networks with bounded width, and introduce a new decision problem for Bayesian networks, the maximin a posteriori. We present new links between the Bayesian and credal n...

A current popular approach to representing time in Bayesian belief networks is through Dynamic Bayesian Networks (DBNs) (Dean & Kanazawa 1989). DBNs connect sequences of entire Bayes networks, each representing a situation at a snapshot in time. We present an alternative method for incorporating time into Bayesian belief networks that utilizes abstractions of temporal representation. This metho...

This paper discusses the use of various scoring metrics in the Bayesian optimization algorithm BOA which uses Bayesian networks to model promising solutions and generate the new ones The use of decision graphs in Bayesian networks to improve the performance of the BOA is proposed To favor simple models a complexity measure is incorporated into the Bayesian Dirichlet metric for Bayesian networks...