2013/02/06 by Fábio Gagliardi Cozman, Fabio Gagliardi Cozman, Cozman, Fabio Gagliardi
Computer Science · Economics, Econometrics and Finance · Mathematics · #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #Economic and Environmental Valuation #FOS: Computer and information sciences #Health Systems, Economic Evaluations, Quality of Life #Statistical Methods and Bayesian Inference #cs.AI
paper · pdf · doi:10.48550/arxiv.1302.1531
Appears in Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence (UAI1997)
arxiv created 2013/02/06 · openalex publication_date 2013/02/06 · arxiv updated 2013/02/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.