Bayesian Networks, 2nd Edition

Preface to the First Edition

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 hierarchical models for phenomena that involve many interrelated components, using the most appropriate probability distribution for each of them. However, all these capabilities ...