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Bayesian method for learning graphical models with incompletely categorical data

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Publication:956745
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DOI10.1016/S0167-9473(03)00066-5zbMath1429.62037OpenAlexW2065594113MaRDI QIDQ956745

Yang-Bo He, Qiang Zhao, Zhi Geng, Xue-Li Wang

Publication date: 26 November 2008

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0167-9473(03)00066-5

zbMATH Keywords

incomplete dataBayesian learningposterior meangraphical modelhyper Markov law


Mathematics Subject Classification ID

Point estimation (62F10) Bayesian inference (62F15) Graphical methods in statistics (62A09)


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Cites Work

  • Unnamed Item
  • Markov fields and log-linear interaction models for contingency tables
  • Hyper Markov laws in the statistical analysis of decomposable graphical models
  • Bayesian analysis in expert systems. With comments and a rejoinder by the authors
  • Learning Bayesian networks: The combination of knowledge and statistical data
  • Mixed Graphical Models with Missing Data and the Partial Imputation EM Algorithm
  • Graphical and Recursive Models for Contingency Tables
  • Sequential updating of conditional probabilities on directed graphical structures
  • Bayesian Methods for Censored Categorical Data
  • Probabilistic Networks and Expert Systems
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