Resumen de Probabilistic abduction without priors
Didier Dubois, Angelo Gilio, Gabriele Kern-Isberner
This paper considers the simple problem of abduction in the framework of Bayes theorem, when the prior probability of the hypothesis is not available, either because there are no statistical data to rely on, or simply because a human expert is reluctant to provide a subjective assessment of this prior probability. This abduction problem remains an open issue since a simple sensitivity analysis on the value of the unknown prior yields empty results. This paper tries to propose some criteria a solution to this problem should satisfy. It then surveys and comments on various existing or new solutions to this problem: the use of likelihood functions (as in classical statistics), the use of information principles like maximum entropy, Shapley value, maximum likelihood. Finally, we present a novel maximum likelihood solution by making use of conditional event theory. The formal setting includes de Finetti�s coherence approach, which does not exclude conditioning on contingent events with zero probability.
