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A survey of Bayesian Data Mining - Part I: Discrete and semi-discrete Data Matrices
RISE - Research Institutes of Sweden, ICT, SICS.
1999 (English)Report (Other academic)
Abstract [en]

This tutorial summarises the use of Bayesian analysis and Bayes factors for finding significant properties of discrete (categorical and ordinal) data. It overviews methods for finding dependencies and graphical models, latent variables, robust decision trees and association rules.

Place, publisher, year, edition, pages
Swedish Institute of Computer Science , 1999, 1. , p. 31
Series
SICS Technical Report, ISSN 1100-3154 ; T99:08
Keywords [en]
Bayes Factor, Graphical Model, Mixture model, Dependency
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:ri:diva-21971OAI: oai:DiVA.org:ri-21971DiVA, id: diva2:1041513
Available from: 2016-10-31 Created: 2016-10-31 Last updated: 2018-02-09Bibliographically approved

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fulltext(317 kB)110 downloads
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File name FULLTEXT01.pdfFile size 317 kBChecksum SHA-512
60c9e241997c4fb00f8eaf3aa05b19807c6e5b26dc3e6b528ec5d1a34a92209888ba8f47282345ae5c11330dd7753c89e2a6208c9002e1ec31fb340c50a66ed5
Type fulltextMimetype application/pdf

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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Language
  • de-DE
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Output format
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