Building probabilistic networks: Where do the numbers come from? - a guide to the literature

Abstract

Probabilistic networks are now fairly well established as practical representations of knowl-edge for reasoning under uncertainty, as demonstrated by an increasing number of success-ful 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 rela-tionships between them, and a quantitative part, encoding probabilities over the variables [Pearl, 1988].