Learning hybrid Bayesian networks using mixtures of truncated exponentials

Authors:
Vanessa Romero;Rafael Rumí;Antonio Salmerón
Affiliations:
Cajamar Savings Bank, Plaza Barcelona 5, 04006 Almería, Spain;Department of Statistics and Applied Mathematics, University of Almería, La Cañada de San Urbano s/n, 04120 Almería, Spain;Department of Statistics and Applied Mathematics, University of Almería, La Cañada de San Urbano s/n, 04120 Almería, Spain
Venue:
International Journal of Approximate Reasoning
Year:
2006

Citing 7
Cited 16

A Bayesian Method for the Induction of Probabilistic Networks from Data

Machine Learning
Importance sampling in Bayesian networks using probability trees

Computational Statistics & Data Analysis
Expert Systems and Probabiistic Network Models

Expert Systems and Probabiistic Network Models
Induction of Decision Trees

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
Monte Carlo Statistical Methods (Springer Texts in Statistics)

Monte Carlo Statistical Methods (Springer Texts in Statistics)
Nonuniform dynamic discretization in hybrid networks

UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence

Learning algorithm of wavelet network based on sampling theory

Neurocomputing
Belief update in CLG Bayesian networks with lazy propagation

International Journal of Approximate Reasoning
Bayesian classifiers based on kernel density estimation: Flexible classifiers

International Journal of Approximate Reasoning
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
Tree augmented naive Bayes for regression using mixtures of truncated exponentials: application to higher education management

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
Extended Shenoy--Shafer architecture for inference in hybrid bayesian networks with deterministic conditionals

International Journal of Approximate Reasoning
Review: Bayesian networks in environmental modelling

Environmental Modelling & Software
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
Learning mixtures of polynomials of multidimensional probability densities from data using B-spline interpolation

International Journal of Approximate Reasoning
Learning mixtures of truncated basis functions from data

International Journal of Approximate Reasoning

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper we introduce an algorithm for learning hybrid Bayesian networks from data. The result of the algorithm is a network where the conditional distribution for each variable is a mixture of truncated exponentials (MTE), so that no restrictions on the network topology are imposed. The structure of the network is obtained by searching over the space of candidate networks using optimisation methods. The conditional densities are estimated by means of Gaussian kernel densities that afterwards are approximated by MTEs, so that the resulting network is appropriate for using standard algorithms for probabilistic reasoning. The behaviour of the proposed algorithm is tested using a set of real-world and artificially generated databases.