Extension of Bayesian Network Classifiers to Regression Problems
IBERAMIA '08 Proceedings of the 11th Ibero-American conference on AI: Advances in Artificial Intelligence
Arc reversals in hybrid Bayesian networks with deterministic variables
International Journal of Approximate Reasoning
Inference in Hybrid Bayesian Networks with Deterministic Variables
ECSQARU '09 Proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Maximum Likelihood Learning of Conditional MTE Distributions
ECSQARU '09 Proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
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
Efficiency of influence diagram models with continuous decision variables
Decision Support Systems
IDA'07 Proceedings of the 7th international conference on Intelligent data analysis
Parameter estimation and model selection for mixtures of truncated exponentials
International Journal of Approximate Reasoning
Hybrid Bayesian network classifiers: Application to species distribution models
Environmental Modelling & Software
Inference in hybrid Bayesian networks using mixtures of polynomials
International Journal of Approximate Reasoning
International Journal of Approximate Reasoning
A re-definition of mixtures of polynomials for inference in hybrid Bayesian networks
ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
Mixtures of truncated basis functions
International Journal of Approximate Reasoning
Nonlinear deterministic relationships in bayesian networks
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Two issues in using mixtures of polynomials for inference in hybrid Bayesian networks
International Journal of Approximate Reasoning
Answering queries in hybrid Bayesian networks using importance sampling
Decision Support Systems
Inventory management with log-normal demand per unit time
Computers and Operations Research
International Journal of Approximate Reasoning
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Mixtures of truncated exponentials (MTE) potentials are an alternative to discretization and Monte Carlo methods for solving hybrid Bayesian networks. Any probability density function (PDF) can be approximated by an MTE potential, which can always be marginalized in closed form. This allows propagation to be done exactly using the Shenoy-Shafer architecture for computing marginals, with no restrictions on the construction of a join tree. This paper presents MTE potentials that approximate standard PDF's and applications of these potentials for solving inference problems in hybrid Bayesian networks. These approximations will extend the types of inference problems that can be modelled with Bayesian networks, as demonstrated using three examples.