Aggregating Learned Probabilistic Beliefs
  We consider the task of aggregating beliefs of severalexperts. We assume that
these beliefs are represented as probabilitydistributions. We argue that the
evaluation of any aggregationtechnique depends on the semantic context of this
task. We propose aframework, in which we assume that nature generates samples
from a`true' distribution and different experts form their beliefs based onthe
subsets of the data they have a chance to observe. Naturally, theideal
aggregate distribution would be the one learned from thecombined sample sets.
Such a formulation leads to a natural way tomeasure the accuracy of the
aggregation mechanism.We show that the well-known aggregation operator LinOP is
ideallysuited for that task. We propose a LinOP-based learning
algorithm,inspired by the techniques developed for Bayesian learning,
whichaggregates the experts' distributions represented as Bayesiannetworks. Our
preliminary experiments show that this algorithmperforms well in practice.
