Partial least-squares modeling of continuous nodes in Bayesian networks

In Bayesian networks it is necessary to compute relationships between continuous nodes. The standard Bayesian network methodology represents this dependency with a linear regression model whose parameters are estimated by a maximum likelihood (ML) calculation. Partial least-squares (PLS) is proposed as an alternative method for computing the model parameters. This new hybrid method is termed PLS-Bayes, as it uses PLS to calculate regression vectors for a Bayesian network. This alternative approach requires storing the raw data matrix rather than sequentially updating sufficient statistics, but results in a regression matrix that predicts with higher accuracy, requires less training data, and performs well in large networks. (C) 2003 Elsevier Science B.V. All rights reserved.