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Physiological mechanisms in human biology, the progress of disease in individual patients, hospital work-flow management: these are just a few of the many complicated processes studied by researchers in biomedicine and health-care. For controlling the ever increasing complexity of these fields, a proper understanding of their processes is important as is the ability to reason about them. The characteristics of the processes vary widely; however, typically only part of all the factors by which they are governed can be observed in practice. The processes, moreover, include the effects of individual as well as random variation. Essentially they are uncertain; the uncertainties involved render an overall understanding hard to achieve and reasoning a daunting task. Models capturing these processes and methods for using these models are thus called for to support decisionmaking in real-life practice.

Bayesian networks …