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Bayesian Networks for Data Mining by Fayyad U. is a nonfiction available to read on EtoBox.

What is Bayesian Networks for Data Mining about?

A Bayesian network is a graphical model that encodes probabilistic relationships among variables of interest. When used in conjunction with statistical techniques, the graphical model has several advantages for data modeling. One, because the model encodes dependencies among all variables, it readily handles situations where some data entries are missing. Two, a Bayesian network can be used to learn causal relationships, andhence can be used to gain understanding about a problem domain and to predict the consequences of intervention. Three, because the model has both a causal and probabilistic semantics, it is an ideal representation for combining prior knowledge (which often comes in causal form) and data. Four, Bayesian statistical methods in conjunction with Bayesian networks offer an efficient and principled approach for avoiding the overfitting of data. In this paper, we discuss methods for constructing Bayesian networks from prior knowledge and summarize Bayesian statistical methods for using data to improve these models. With regard to the latter task, we describe methodsfor learning both the parameters and structure of a Bayesian network, including techniques for learning w

Who reads Bayesian Networks for Data Mining?

Author

Fayyad U.

Published

1997

Language

EN

Category

nonfiction

Subjects

Computer Science, Organization And Data Processing, Stem