Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with strong assumptions of independence among features, called naive Bayes, is competitive withâ€¦ (More)

We describe a Bayesian approach for learning Bayesian networks from a combination of prior knowledge and statistical data. First and foremost, we develop a methodology for assessing informativeâ€¦ (More)

We describe scoring metrics for learning Bayesian networks from a combination of user knowledge and statistical data. PreviÂ ous work has concentrated on metrics for doÂ mains containing onlyâ€¦ (More)

JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology andâ€¦ (More)

Algorithms for learning Bayesian networks from data have two components: a scoring metric and a search procedure. The scoring metric computes a score reeecting the goodness-of-t of the structure toâ€¦ (More)

This paper explores the role of Directed Acyclic Graphs (DAGs) as a representation of conditionÂ al independence relationships. We show that DAGs offer polynomially sound and complete inferenceâ€¦ (More)

We consider the problem of performing Nearest-neighbor queries efficiently over large high-dimensional databases. To avoid a full database scan, we target constructing a multidimensional indexâ€¦ (More)

MOTIVATION
Genetic linkage analysis is a useful statistical tool for mapping disease genes and for associating functionality of genes with their location on the chromosome. There is a need for aâ€¦ (More)

We formulate necessary and sufficient conditions for an arbitrary discrete probability distribution to factor according to an undirected graphical model, or a log-linear model, or other more generalâ€¦ (More)