Robustness Analysis of Bayesian Networks with Local Convex Sets of Distributions
This work addresses uncertainty in probabilistic models for applications like decision-making, but it is incremental as it builds on existing robust inference techniques.
The paper tackles robust Bayesian inference in Bayesian networks by modeling perturbations as local convex sets of distributions, reducing the problem to linear programming and interior-point optimization to compute posterior bounds, with methods for expected utilities and variances.
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.