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FOUNDATIONAL PRINCIPLES FOR LARGE SCALE INFERENCE ...
FOUNDATIONAL PRINCIPLES FOR LARGE SCALE INFERENCE : ILLUSTRATIONS THROUGH CORRELATION MINING By Alfred O. Hero Bala Rajaratnam Technical Report No. 2015-13 May 2015 Department of Statistics STANFORD UNIVERSITY Stanford, California 94305-4065 FOUNDATIONAL PRINCIPLES FOR LARGE SCALE INFERENCE : ILLUSTRATIONS THROUGH CORRELATION MINING By Alfred O. Hero University of Michigan Ann Arbor Bala Rajaratnam Stanford University Technical Report No. 2015-13 May 2015 This research was supported in part by National Science Foundation grants DMS 0906392, CMG 1025465, AGS 1003823, and DMS 1106642. Department of Statistics STANFORD UNIVERSITY Stanford, California 94305-4065 FOUNDATIONAL PRINCIPLES for LARGE SCALE INFERENCE :Illustrations through correlation miningAlfred O. Hero and Bala Rajaratnam University of Michigan, Ann Arbor, MI 48109-2122, USA Stanford University, Stanford, CA 94305-4065, USAA bstractWhen can reliable INFERENCE be drawn in the Big Data context?
of inference on Big Data from the point of view of statistical reproducibility. The statistical reproducibility point of view is founded on a non-monolithic notion of Big Data: the data should be considered as a matrix with pcolumns and nrows indexed by,
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