实验数据的概率密度函数的最优估计
Optimal estimate of probability density functions from experimental data
摘要：
提出了一种由时间序列提供概率密度函数（PDF）最优估计的方法。它允许几乎任意分辨率的PDF应用于采样分析函数或实验数字化数据。当结果与使用标准直方图方法计算的相同数据的PDF进行比较时，观察到显著的改善，特别是在PDF的远侧区域，其中低概率事件提供差的统计。
英文摘要：
A method providing optimal estimate of probability density functions (PDFs) from time series is proposed. It allows almost arbitrary resolution PDFs when applied to either, sampled analytic functions or digitized data from experiments. When results are compared with PDFs of the same data calculated using the standard histogram method, a remarkable improvement is observed, especially in far lateral regions of the PDF, where low probability events give poor statistics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
