A probability density function generator based on neural networks
A probability density function generator based on neural networks
复制标题
基于神经网络的概率密度函数发生器
DOI：
10.1016/j.physa.2019.123344
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发表时间：
2020-03-01
影响因子：
3.3
通讯作者：
Wu, Ling
中科院分区：
文献类型：
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作者：
Chen, Chi-Hua;Song, Fangying;Wu, Ling
为了生成用于拟合实际数据的概率分布的概率密度函数（PDF），本研究提出了一种深度学习方法，该方法包括两个阶段：（1）用于估计累积分布函数（CDF）的训练阶段和（2）用于预测相应PDF的执行阶段。常见概率分布的CDF可以用作所提出的深度学习模型的隐藏层中的激活函数，用于学习实际累积概率，并且训练的深度学习模型的微分方程可以用于估计PDF。数值实验与单一和混合分布进行评估所提出的方法的性能。实验结果表明，该方法可以准确地估计CDF和PDF的值。(C)2019爱思唯尔B.V.保留所有权利。
In order to generate a probability density function (PDF) for fitting the probability distributions of practical 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 utilised as activation functions in the hidden layers of the proposed deep learning model for learning actual cumulative probabilities, and the differential equation of the trained deep learning model can be used to estimate the PDF. Numerical experiments with single and mixed distributions are conducted to evaluate the performance of the proposed method. The experimental results show that the values of both CDF and PDF can be precisely estimated by the proposed method. (C) 2019 Elsevier B.V. All rights reserved.