Modeling conditional distributions of continuous variables in bayesian networks

Authors:
Barry R. Cobb;Rafael Rumí;Antonio Salmerón
Affiliations:
Department of Economics and Business, Virginia Military Institute, Lexington, VA;Department of Statistics and Applied Mathematics, University of Almería, Almería, Spain;Department of Statistics and Applied Mathematics, University of Almería, Almería, Spain
Venue:
IDA'05 Proceedings of the 6th international conference on Advances in Intelligent Data Analysis
Year:
2005

Citing 3
Cited 1

Stable local computation with conditional Gaussian distributions

Statistics and Computing
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
Nonuniform dynamic discretization in hybrid networks

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

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

Quantified Score

Hi-index	0.00

Visualization

Abstract

The MTE (mixture of truncated exponentials) model was introduced as a general solution to the problem of specifying conditional distributions for continuous variables in Bayesian networks, especially as an alternative to discretization. In this paper we compare the behavior of two different approaches for constructing conditional MTE models in an example taken from Finance, which is a domain were uncertain variables commonly have continuous conditional distributions.