The Robotics Institute

Carnegie Mellon Robotics Institute

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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.

Notes

Sponsor: NASA

Grant ID: NAGW-1175

Number of pages: 8

Text Reference

Fabio Cozman, "Robustness Analysis of Bayesian Networks with Global Neighborhoods," tech. report CMU-RI-TR-96-42, Robotics Institute, Carnegie Mellon University, January, 1997

BibTeX Reference

@techreport{Cozman_1997_434,
   author = "Fabio Cozman",
   title = "Robustness Analysis of Bayesian Networks with Global Neighborhoods",
   booktitle = "",
   institution = "Robotics Institute",
   month = "January",
   year = "1997",
   number= "CMU-RI-TR-96-42",
   address= "Pittsburgh, PA",
}

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