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Probabilistic Event Detection using Data Fusion

PA Radon EPADr. Saumyadipta Pyne and collaborators (Prof. Benjamin Kedem and Xuze Zhang, University of Maryland) have developed a new statistical framework for real and synthetic data fusion to estimate exceedance probabilities in an observed stream of events with only a few observations. Starting with a baseline distribution,​ this method can model a dynamic distortion of that original template, and thus, be used for modeling environmental exposures. The study was published in 'Applied Stochastic Models in Business and Industry' in June 2020, and covered in press. A follow-up study addressed the problem of model selection in such data fusion. Image courtesy: US EPA.

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