Robustness Analysis of Bayesian Networks with Local Convex Sets of Distributions

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 rst 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-pointoptimizationmethodsto generate posterior bounds and how to generate approximationsthat are guaranteed to converge to correct posterior bounds. We also discuss calculation of bounds for expected utilities and variances, and global perturbation models.

• Type: Conference
• Year: 1997
• Where: UAI
• Authors: Fabio Gagliardi Cozman
• Keywords: Joint Distributions, Posterior Bounds, Posterior Probability Bounds, UAI 1997, UAI 2008