Robustness Analysis of Bayesian Networks with Global Neighborhoods
Tech. Report, CMU-RI-TR-96-42, Robotics Institute, Carnegie Mellon University, 1997
Abstract
This paper presents algorithms for robustness analysis of Bayesian networks with global neighborhoods. Robust Bayesian inference is the calculation of bounds on posterior values given perturbations in a probabilistic model. We present algorithms for robust inference (including expected utility, expected value and variance bounds) with global perturbations that can be modeled by \epsilon-contaminated, constant density ratio, constant density bounded and total variation classes of distributions.
BibTeX
@techreport{Cozman-1997-14299,author = {Fabio Cozman},
title = {Robustness Analysis of Bayesian Networks with Global Neighborhoods},
year = {1997},
month = {January},
institute = {Carnegie Mellon University},
address = {Pittsburgh, PA},
number = {CMU-RI-TR-96-42},
}
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