This paper investigates local models; credal sets are associated only
to marginal or conditional nodes in a network. A different
type of Quasi-Bayesian network can be defined through *global*
perturbations acting on the whole joint distribution. Several
classes of distributions can be used to define such global
perturbations (Appendix), but some of them are
advantageous from an algorithmic point of view. The
epsi-contaminated, constant density ratio, constant density
bounded and total variation classes lead to robust inferences whose
complexity is identical to the complexity of standard Bayesian
inferences (the algorithms are presented
in [11]). Future work will
reveal whether such global models are useful for practical robustness analysis.

© Fabio Cozman[Send Mail?]

Fri May 30 15:55:18 EDT 1997