Robustness Analysis of Bayesian Networks with Local Convex Sets of Distributions

TL;DR

This paper develops methods for robust Bayesian inference in Bayesian networks by using local convex sets of distributions, enabling efficient computation of posterior bounds under model perturbations.

Contribution

It introduces two approaches for combining local models—linear programming and interior-point methods—to compute posterior bounds in robust Bayesian inference.

Findings

Linear programming reduces robust inference to a tractable problem.

Interior-point methods effectively generate posterior bounds and approximations.

Methods extend to bounds on expected utilities and variances.

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.