A Bayesian belief network (BBN) is a modeling and knowledge-representation structure used in artificial intelligence that consists of a graphical model depicting probabilistic relationships among variables of interest. This graphical model is a valuable tool for representing the causal relationships in a given set of variables. Because the number of possible BBNs for a given data set is exponential with respect to the number of variables, learning a BBN from data is a difficult and resource-consuming task. A greedy algorithm that automatically constructs a BBN from a data set of cases obtained from a household survey was implemented. The resulting BBN shows the dependencies among key variables that are associated with the trip-generation process.
Learning a Causal Model from Household Survey Data by Using a Bayesian Belief Network
Transportation Research Record
Transportation Research Record: Journal of the Transportation Research Board ; 1836 , 1 ; 29-36
2003-01-01
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Learning a Causal Model from Household Survey Date by Using a Bayesian Belief Network
Online Contents | 2003
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