Uncertainty in Artificial Intelligence
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Robustness Analysis of Bayesian Networks with Local Convex Sets of Distributions
Fabio Cozman
Abstract:
Robust Bayesian inference is the calculation of posterior probability bounds given perturbations in a probabilistic model. This paper focuses on perturbations that can be expressed locally in Bayesian networks through convex sets of distributions. Two approaches for combination of local models are considered. The first approach takes the largest set of joint distributions that is compatible with the local sets of distributions; we show how to reduce this type of robust inference to a linear programming problem. The second approach takes the convex hull of joint distributions generated from the local sets of distributions; we demonstrate how to apply interior-point optimization methods to generate posterior bounds and how to generate approximations that are guaranteed to converge to correct posterior bounds. We also discuss calculation of bounds for expected utilities and variances, and global perturbation models.
Keywords: Convex sets of probability, robust statistics, graphical models, expected loss (util
Pages: 108-115
PS Link: http://www.cs.cmu.edu/~fgcozman/Research/QuasiBayesian/UAI97/uai97.ps
PDF Link: /papers/97/p108-cozman.pdf
BibTex:
@INPROCEEDINGS{Cozman97,
AUTHOR = "Fabio Cozman ",
TITLE = "Robustness Analysis of Bayesian Networks with Local Convex Sets of Distributions",
BOOKTITLE = "Proceedings of the Thirteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-97)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1997",
PAGES = "108--115"
}


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