LAZY propagation: a junction tree inference algorithm based on lazy evaluation
Artificial Intelligence
The optimal discretization of probability density functions
Computational Statistics & Data Analysis - Special issue on parallel processing and statistics
Importance sampling in Bayesian networks using probability trees
Computational Statistics & Data Analysis
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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IPMU'94 Selected papers from the 5th International Conference on Processing and Management of Uncertainty in Knowledge-Based Systems, Advances in Intelligent Computing
Mixtures of Truncated Exponentials in Hybrid Bayesian Networks
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Axioms for probability and belief-function proagation
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Hybrid influence diagrams using mixtures of truncated exponentials
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Nonuniform dynamic discretization in hybrid networks
UAI'97 Proceedings of the Thirteenth conference on Uncertainty 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
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Mixtures of truncated exponential (MTE) networks are a powerful alternative to discretisation when working with hybrid Bayesian networks. One of the features of the MTE model is that standard propagation algorithm can be used. In this paper we propose an approximate propagation algorithm for MTE networks which is based on the Penniless propagation method already known for discrete variables. The performance of the proposed method is analysed in a series of experiments with random networks.