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Estimation of Tail Probabilities by Repeated Augmented Reality

Synthetic data, when properly used, can enhance patterns in real data and thus provide insights into different problems. Here, the estimation of tail probabilities of rare events from a moderately large number of observations is considered. The problem is approached by a large number of augmentation...

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Detalles Bibliográficos
Autores principales: Kedem, Benjamin, Pyne, Saumyadipta
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7816841/
https://www.ncbi.nlm.nih.gov/pubmed/33495693
http://dx.doi.org/10.1007/s42519-020-00152-1
Descripción
Sumario:Synthetic data, when properly used, can enhance patterns in real data and thus provide insights into different problems. Here, the estimation of tail probabilities of rare events from a moderately large number of observations is considered. The problem is approached by a large number of augmentations or fusions of the real data with computer-generated synthetic samples. The tail probability of interest is approximated by subsequences created by a novel iterative process. The estimates are found to be quite precise.