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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    Title :

    Learning a Causal Model from Household Survey Data by Using a Bayesian Belief Network


    Additional title:

    Transportation Research Record


    Contributors:


    Publication date :

    2003-01-01




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





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