Robustness of Bayesian Analyses

Joseph B. Kadane0444862099, 9780444862099

This paper presents algorithms for robustness analysis of Bayesian networks with global neighborhoods. Robust Bayesian inference is the calculation of bounds on posterior valuesgiven 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 ffl-contaminated, constant density ratio, constant density bounded and total variation classes of distributions.