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
Simulation and the Monte Carlo Method
Simulation and the Monte Carlo Method
Causal Probabilistic Networks with Both Discrete and Continuous Variables
IEEE Transactions on Pattern Analysis and Machine Intelligence
Heuristic Algorithms for the Triangulation of Graphs
IPMU'94 Selected papers from the 5th International Conference on Processing and Management of Uncertainty in Knowledge-Based Systems, Advances in Intelligent Computing
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
Belief update in CLG Bayesian networks with lazy propagation
International Journal of Approximate Reasoning
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
IDA'07 Proceedings of the 7th international conference on Intelligent data analysis
Hybrid Bayesian network classifiers: Application to species distribution models
Environmental Modelling & Software
Approximate inference in Bayesian networks using binary probability trees
International Journal of Approximate Reasoning
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
Learning mixtures of truncated basis functions from data
International Journal of Approximate Reasoning
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Mixtures of truncated exponentials (MTEs) are a powerful alternative to discretisation when working with hybrid Bayesian networks. One of the features of the MTE model is that standard propagation algorithms can be used. However, the complexity of the process is too high and therefore approximate methods, which tradeoff complexity for accuracy, become necessary. 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. We also consider how to use Markov Chain Monte Carlo to carry out the probability propagation. The performance of the proposed methods is analysed in a series of experiments with random networks.