Evidential reasoning using stochastic simulation of causal models
Artificial Intelligence
LAZY propagation: a junction tree inference algorithm based on lazy evaluation
Artificial Intelligence
Importance sampling in Bayesian networks using probability trees
Computational Statistics & Data Analysis
Simulation and the Monte Carlo Method
Simulation and the Monte Carlo Method
Stable local computation with conditional Gaussian distributions
Statistics and Computing
Causal Probabilistic Networks with Both Discrete and Continuous Variables
IEEE Transactions on Pattern Analysis and Machine Intelligence
Axioms for probability and belief-function proagation
UAI '88 Proceedings of the Fourth Annual Conference on Uncertainty in Artificial Intelligence
A general algorithm for approximate inference and its application to hybrid bayes nets
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Nonuniform dynamic discretization in hybrid networks
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Extension of Bayesian Network Classifiers to Regression Problems
IBERAMIA '08 Proceedings of the 11th Ibero-American conference on AI: Advances in Artificial Intelligence
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
Learning hybrid Bayesian networks using mixtures of truncated exponentials
International Journal of Approximate Reasoning
Parameter estimation and model selection for mixtures of truncated exponentials
International Journal of Approximate Reasoning
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
Review: Bayesian networks in environmental modelling
Environmental Modelling & Software
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
Penniless propagation with mixtures of truncated exponentials
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Modeling conditional distributions of continuous variables in bayesian networks
IDA'05 Proceedings of the 6th international conference on Advances in Intelligent Data Analysis
Availability modelling of repairable systems using Bayesian networks
Engineering Applications of Artificial Intelligence
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
Modelling and inference with Conditional Gaussian Probabilistic Decision Graphs
International Journal of Approximate Reasoning
Inventory management with log-normal demand per unit time
Computers and Operations Research
Domains of competence of the semi-naive Bayesian network classifiers
Information Sciences: an International Journal
International Journal of Approximate Reasoning
Learning mixtures of truncated basis functions from data
International Journal of Approximate Reasoning
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In this paper we propose the use of mixtures of truncated exponential (MTE) distributions in hybrid Bayesian networks. We study the properties of the MTE distribution and show how exact probability propagation can be carried out by means of a local computation algorithm. One feature of this model is that no restriction is made about the order among the variables either discrete or continuous. Computations are performed over a representation of probabilistic potentials based on probability trees, expanded to allow discrete and continuous variables simultaneously. Finally, a Markov chain Monte Carlo algorithm is described with the aim of dealing with complex networks.