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
Mixtures of Truncated Exponentials in Hybrid Bayesian Networks
ECSQARU '01 Proceedings of the 6th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Approximate probability propagation with mixtures of truncated exponentials
International Journal of Approximate Reasoning
IDA'07 Proceedings of the 7th international conference on Intelligent data analysis
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
Hybrid Bayesian network classifiers: Application to species distribution models
Environmental Modelling & Software
Review: Bayesian networks in environmental modelling
Environmental Modelling & Software
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In this paper we explore the extension of various Bayesian network classifiers to regression problems where some of the explanatory variables are continuous and some others are discrete. The goal is to compute the posterior distribution of the response variable given the observations, and then use that distribution to give a prediction. The involved distributions are represented as Mixtures of Truncated Exponentials. We test the performance of the proposed models on different datasets commonly used as benchmarks, showing a competitive performace with respect to the state-of-the-art methods.