**Fabio Cozman
Robotics Institute, School of Computer Science,
Carnegie Mellon University
fgcozman@cs.cmu.edu, http://www.cs.cmu.edu/~fgcozman**

This work appeared in the Proceedings of the Conference on Uncertainty in Artificial Intelligence, 1997. A postscript version is available.

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.

- INTRODUCTION
- LOCAL ROBUSTNESS ANALYSIS OF BAYESIAN NETWORKS
- JOINT CREDAL SETS BY NATURAL EXTENSION
- TYPE-1 JOINT CREDAL SETS
- EXPECTED UTILITY AND VARIANCE
- LOCAL ROBUSTNESS ANALYSIS IN
*JAVABAYES* - LOCAL vs. GLOBAL MODELS
- CONCLUSION
- Classes of polytopic credal sets
- References

© Fabio Cozman[Send Mail?]

Fri May 30 15:55:18 EDT 1997