Learning a causal model from household survey data by using a bayesian belief network.

Author(s)
Torres, F.J. & Huber, M.
Year
Abstract

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.

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Publication

Library number
C 32926 (In: C 32921 S [electronic version only]) /72 / ITRD E828135
Source

Transportation Research Record. 2003. (1836) pp29-36 (5 Fig., 5 Tab., 6 Ref.)

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