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Bayesian Networks Representations, Generalized Imputation, And Synthetic Micro-Data Satisfying Analytic Constraints

By U. S. Census Bureau Department

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Book Id: WPLBN0000585789
Format Type: PDF eBook
File Size: 216,767 KB.
Reproduction Date: 2005

Title: Bayesian Networks Representations, Generalized Imputation, And Synthetic Micro-Data Satisfying Analytic Constraints  
Author: U. S. Census Bureau Department
Volume:
Language: English
Subject: Government publications, Census., Census report
Collections: U.S. Census Bureau Collection
Historic
Publication Date:
Publisher: U.S. Census Bureau Department

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Bureau Department, U. C. (n.d.). Bayesian Networks Representations, Generalized Imputation, And Synthetic Micro-Data Satisfying Analytic Constraints. Retrieved from http://www.worldlibrary.in/


Description
Statistical Reference Document

Excerpt
Excerpt: Graphical representation of Bayes Nets and other probabilistic relationships date to Lauritzen and Spiegelhalter (1988). They are used extensively in machine learning. For instance, Figure 2 in Getoor et al. (2001) (reprinted below) demonstrates an efficient representation of Census data. 951 parameters are able to represent a potentially large number of cells in a contingency table (7 billion).

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