Operations for inference in continuous Bayesian networks with linear deterministic variables

Authors:
Barry R. Cobb;Prakash P. Shenoy
Affiliations:
Department of Economics and Business, Virginia Military Institute, Lexington, VA 24450, USA;School of Business, University of Kansas, 1300 Sunnyside Ave., Summerfield Hall, Lawrence, KS 66045-7585, USA
Venue:
International Journal of Approximate Reasoning
Year:
2006

Citing 4
Cited 7

Probabilistic Networks and Expert Systems

Probabilistic Networks and Expert Systems
Stable local computation with conditional Gaussian distributions

Statistics and Computing
Inference in hybrid Bayesian networks with mixtures of truncated exponentials

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

Belief update in CLG Bayesian networks with lazy propagation

International Journal of Approximate Reasoning
Arc reversals in hybrid Bayesian networks with deterministic variables

International Journal of Approximate Reasoning
Conditional independence structure and its closure: Inferential rules and algorithms

International Journal of Approximate Reasoning
VC dimension and inner product space induced by Bayesian networks

International Journal of Approximate Reasoning
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
Acyclic directed graphs representing independence models

International Journal of Approximate Reasoning
On the properties of concept classes induced by multivalued Bayesian networks

Information Sciences: an International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

An important class of continuous Bayesian networks are those that have linear conditionally deterministic variables (a variable that is a linear deterministic function of its parents). In this case, the joint density function for the variables in the network does not exist. Conditional linear Gaussian (CLG) distributions can handle such cases when all variables are normally distributed. In this paper, we develop operations required for performing inference with linear conditionally deterministic variables in continuous Bayesian networks using relationships derived from joint cumulative distribution functions. These methods allow inference in networks with linear deterministic variables and non-Gaussian distributions.