Learning of Optimal Forecast Aggregation in Partial Evidence Environments

arXiv: Learning, Volume abs/1802.07107, 2018.

Cited by: 1|Bibtex|Views7
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Other Links: dblp.uni-trier.de|academic.microsoft.com|arxiv.org

Abstract:

consider the forecast aggregation problem in repeated settings, where the forecasts are done on a binary event. At each period multiple experts provide forecasts about an event. The goal of the aggregator is to aggregate those forecasts into a subjective accurate forecast. assume that experts are Bayesian; namely they share a common pri...More

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