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Neural Networks for Classifying Probability Distributions

Published

Author(s)

Siham Khoussi, N. Alan Heckert, Abdella Battou, Saddek Bensalem

Abstract

Probability distribution fitting of an unknown stochastic process is an important preliminary step for any further analysis in science or engineering. However, it requires some background in statistics and prior considerations of the process or phenomenon under study. As such, this paper presents an alternative approach which doesn't require prior knowledge of statistical methods nor previous assumption on the available data. Instead, using Deep Learning, the correct distributional model is extracted from the output of a neural network that was previously trained on a large suitable dataset in order to classify any array of observations into a matching distributional model. We find that our classifier can perform this task comparably to using maximum likelihood estimation with an Anderson-Darling goodness of fit.
Citation
Technical Note (NIST TN) - 2152
Report Number
2152

Keywords

Deep learning, neural networks, statistics, goodness of fit

Citation

Khoussi, S. , Heckert, N. , Battou, A. and Bensalem, S. (2021), Neural Networks for Classifying Probability Distributions, Technical Note (NIST TN), National Institute of Standards and Technology, Gaithersburg, MD, [online], https://doi.org/10.6028/NIST.TN.2152, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=931221 (Accessed December 8, 2021)
Created April 19, 2021