Abstract
Given a large dataset and an estimation task, it is common to pre-process the data by reducing them to a set of sufficient statistics. This step is often regarded as straightforward and advantageous (in that it simplifies statistical analysis). I show that –on the contrary– reducing data to sufficient statistics can change a computationally tractable estimation problem into an intractable one. I discuss connections with recent work in theoretical computer science, and implications for some techniques to estimate graphical models.
Citation
Andrea Montanari. "Computational implications of reducing data to sufficient statistics." Electron. J. Statist. 9 (2) 2370 - 2390, 2015. https://doi.org/10.1214/15-EJS1059
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