Aggregating Learned Probabilistic Beliefs
This work addresses the challenge of combining expert beliefs in probabilistic settings, which is incremental as it builds on existing aggregation methods like LinOP.
The paper tackles the problem of aggregating probabilistic beliefs from multiple experts by proposing a framework where the ideal aggregate is the distribution learned from combined data, and shows that the LinOP operator is well-suited for this task, with preliminary experiments indicating good performance.
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.