Communication
Version 1
Preserved in Portico This version is not peer-reviewed
A Probability Density Function Generator Based on Deep Learning
Version 1
: Received: 7 November 2018 / Approved: 12 November 2018 / Online: 12 November 2018 (04:00:20 CET)
A peer-reviewed article of this Preprint also exists.
Abstract
To generate a probability density function (PDF) for fitting probability distributions of real data, this study proposes a deep learning method which consists of two stages: (1) a training stage for estimating the cumulative distribution function (CDF) and (2) a performing stage for predicting the corresponding PDF. The CDFs of common probability distributions can be adopted as activation functions in the hidden layers of the proposed deep learning model for learning actual cumulative probabilities, and the differential equation of trained deep learning model can be used to estimate the PDF. To evaluate the proposed method, numerical experiments with single and mixed distributions are performed. The experimental results show that the values of both CDF and PDF can be precisely estimated by the proposed method.
Keywords
probability density function; cumulative distribution function; deep learning
Subject
Computer Science and Mathematics, Information Systems
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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