Building Probabilistic Networks: Where Do the Numbers Come From? -- Guest Editors' Introduction

Probabilistic networks are now fairly well established as practical representations of knowledge for reasoning under uncertainty, as demonstrated by an increasing number of successful applications in such domains as (medical) diagnosis and prognosis, planning, vision, information retrieval, and natural language processing. A probabilistic network (also referred to as a belief network, Bayesian network, or, somewhat imprecisely, causal network) consists of a graphical structure, encoding a domain's variables and the qualitative relationships between them, and a quantitative part, encoding probabilities over the variables [29]. Bui