Sensitivity to hyperprior parameters in Gaussian Bayesian networks

DOI10.1016/j.jmva.2013.10.022zbMath1278.62035OpenAlexW1996814159MaRDI QIDQ392077

Miguel Angel Gómez-Villegas, Rosario Susi, Hilario Navarro, Paloma Maín

Publication date: 13 January 2014

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jmva.2013.10.022

zbMATH Keywords

Kullback-Leibler divergenceBayesian linear regression

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Bayesian inference (62F15) Applications of graph theory (05C90) Robustness and adaptive procedures (parametric inference) (62F35) Reasoning under uncertainty in the context of artificial intelligence (68T37) Statistical aspects of information-theoretic topics (62B10) Directed graphs (digraphs), tournaments (05C20)

Related Items

A novel divergence for sensitivity analysis in Gaussian Bayesian networks

Uses Software

• R
• HdBCS

Cites Work

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• Parameter priors for directed acyclic graphical models and the characterization of several probability distributions
• Extreme inaccuracies in Gaussian Bayesian networks
• Sparse graphical models for exploring gene expression data
• The effect of block parameter perturbations in Gaussian Bayesian networks: sensitivity and robustness
• Sensitivity Analysis in Gaussian Bayesian Networks Using a Divergence Measure
• Bayesian Networks and Decision Graphs
• Probabilistic Networks and Expert Systems
• On Information and Sufficiency

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