## Graphical Models for Probabilistic and Causal Reasoning (1997)

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### BibTeX

@MISC{Pearl97graphicalmodels,

author = {Judea Pearl},

title = {Graphical Models for Probabilistic and Causal Reasoning},

year = {1997}

}

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### Abstract

INTRODUCTION This chapter surveys the development of graphical models known as Bayesian networks, summarizes their semantical basis and assesses their properties and applications to reasoning and planning. Bayesian networks are directed acyclic graphs (DAGs) in which the nodes represent variables of interest (e.g., the temperature of a device, the gender of a patient, a feature of an object, the occurrence of an event) and the links represent causal influences among the variables. The strength of an influence is represented by conditional probabilities that are attached to each cluster of parents-child nodes in the network. Figure 1 illustrates a simple yet typical Bayesian network. It describes the causal relationships among the season of the year (X 1 ), whether rain falls (X 2 ) during the season, whether the sprinkler is on (X 3 ) during that season, whether the pavement would get wet (X<F28.21