An approximation of surprise index as a measure of confidence
Adam T. Zagorecki, Marcin Koźniewski, Marek J. Drużdżel · D-Scholarship@Pitt (University of Pittsburgh) · 2015
Probabilistic graphical models, such as Bayesian networks, are intuitive and theoretically sound tools for modeling uncertainty. A major problem with applying Bayesian networks in practice is that it is hard to judge whether a model fits well a case that it is supposed to solve. One way of expressing a possible dissonance between a model and a case is the surprise index, proposed by Habbema, which expresses the degree of surprise by the evidence given the model. While this measure reflects the intuition that the probability of a case should be judged in the context of a model, it is computationally intractable. In this paper, we propose an efficient way of approximating the surprise index.