
Preface
Applications of Bayesian networks have multiplied in recent years, spanning
such different topics as systems biology, economics, social sciences and medical
informatics. Different aspects and properties of this class of models are crucial
in each field: the possibility of learning causal effects from observational data
in social sciences, where collecting experimental data is often not possible; the
intuitive graphical representation, which provides a qualitative understanding
of pathways in biological sciences; the ability to construct complex hierarchi-
cal models for phenomena that involve many interrelated components, using
the most appropriate ...