Uncertainty in Artificial Intelligence
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Aggregating Learned Probabilistic Beliefs
Pedrito Maynard-Reid II, Urszula Chajewska
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.
Pages: 354-361
PS Link: http://robotics.stanford.edu/~pedmayn/Papers/lagr01.ps
PDF Link: /papers/01/p354-maynard-reid.pdf
AUTHOR = "Pedrito Maynard-Reid II and Urszula Chajewska",
TITLE = "Aggregating Learned Probabilistic Beliefs",
BOOKTITLE = "Proceedings of the Seventeenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-01)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "2001",
PAGES = "354--361"

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