Scalable Algorithms for Aggregating Disparate Forecasts of Probability

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dc.creatorPredd, Joel B.
dc.creatorKulkarni, Sanjeev R.
dc.creatorOsherson, Daniel N.
dc.creatorPoor, H. Vincent
dc.date2006-01-31
dc.date2006-05-01
dc.date.accessioned2026-07-07T08:16:20Z
dc.date.available2026-07-07T08:16:20Z
dc.descriptionIn this paper, computational aspects of the panel aggregation problem are addressed. Motivated primarily by applications of risk assessment, an algorithm is developed for aggregating large corpora of internally incoherent probability assessments. The algorithm is characterized by a provable performance guarantee, and is demonstrated to be orders of magnitude faster than existing tools when tested on several real-world data-sets. In addition, unexpected connections between research in risk assessment and wireless sensor networks are exposed, as several key ideas are illustrated to be useful in both fields.
dc.descriptionTo be presented at the Ninth International Conference on Information Fusion, Florence, Italy, July 10-13, 2006
dc.identifierhttps://arxiv.org/abs/cs/0601131
dc.identifierhttp://arxiv.org/abs/cs/0601131
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/133747
dc.subjectArtificial Intelligence
dc.subjectDistributed, Parallel, and Cluster Computing
dc.subjectInformation Theory
dc.titleScalable Algorithms for Aggregating Disparate Forecasts of Probability
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