Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Real-world applications of Bayesian networks
Communications of the ACM
Learning Bayesian networks from data: an information-theory based approach
Artificial Intelligence
Learning Bayesian Belief Network Classifiers: Algorithms and System
AI '01 Proceedings of the 14th Biennial Conference of the Canadian Society on Computational Studies of Intelligence: Advances in Artificial Intelligence
Data Mining: Practical Machine Learning Tools and Techniques
Data Mining: Practical Machine Learning Tools and Techniques
Comparing Bayesian network classifiers
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
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While most models of location decisions of firms are based on the principle of utility maximizing behavior, the present study assumes that location decisions are just part of business cycle models, in which location is considered along other business decisions. The business model results in a series of location requirements and these are matched against location characteristics. Given this theoretical perspective, the modeling challenge then becomes how to find the match between firm types and the set of location characteristics using observations of the spatial distribution of firms. In this paper, several Bayesian classifier networks are compared in terms of their performance, using a large data set collected for the Netherlands. Results demonstrate that by taking relationships between predictor variables into account the Bayesian classifiers can improve prediction accuracy compared to commonly used decision tree. From a substantive point of view, our results indicate that different sets of urban characteristics and accessibility requirements are relevant to different office types as reflected in the spatial distribution of these office firms.