Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Machine Learning - Special issue on learning with probabilistic representations
Technical Note: Naive Bayes for Regression
Machine Learning
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Machine Learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Approximate probability propagation with mixtures of truncated exponentials
International Journal of Approximate Reasoning
Learning hybrid Bayesian networks using mixtures of truncated exponentials
International Journal of Approximate Reasoning
Modeling conditional distributions of continuous variables in bayesian networks
IDA'05 Proceedings of the 6th international conference on Advances in Intelligent Data Analysis
Extension of Bayesian Network Classifiers to Regression Problems
IBERAMIA '08 Proceedings of the 11th Ibero-American conference on AI: Advances in Artificial Intelligence
Predicting Stock and Portfolio Returns Using Mixtures of Truncated Exponentials
ECSQARU '09 Proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Review: Bayesian networks in environmental modelling
Environmental Modelling & Software
International Journal of Approximate Reasoning
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In this paper we explore the use of Tree Augmented Naive Bayes (TAN) in regression problems where some of the independent variables are continuous and some others are discrete. The proposed solution is based on the approximation of the joint distribution by a Mixture of Truncated Exponentials (MTE). The construction of the TAN structure requires the use of the conditional mutual information, which cannot be analytically obtained for MTEs. In order to solve this problem, we introduce an unbiased estimator of the conditional mutual information, based on Monte Carlo estimation. We test the performance of the proposed model in a real life context, related to higher education management, where regression problems with discrete and continuous variables are common.