微波测不准框架下的蒙特卡罗抽样偏差
Monte Carlo Sampling Bias in the Microwave Uncertainty Framework
摘要：
不确定性传播软件可能会通过各种方式引入未知的无意偏差。这项工作是一个案例研究中的偏差识别和减少在这样一个软件包，微波不确定性框架（MUF）。MUF的一般目的是在蒙特卡罗（MC）基础上提供自动化的多元统计不确定性传播和分析。合并是MUF中的一个关键模块，负责合并原始数据或转换数据，以准确反映数据的变化及其集中趋势。在这项工作中，结合的MC复制的性能进行了分析比较，其规定的设计目标。另一种结构提出了联合的MC复制和它的性能进行了比较，也对联合的设计目标。这些比较是在一个典型的两阶段的情况下，其中接收到的数据首先与共享的系统误差一起转换，然后结合起来，以产生摘要信息。这些比较揭示了有限的条件下，结合的不确定性结果是无偏的，这些偏见的程度时，这些条件不满足。对于小MC样本量，无论是当前的还是替代的结构，都不能完全满足联合收割机的设计目标，也不能始终优于其他结构。然而，对于大MC样本量，建议的替代构造的偏倚渐近为零，建议使用该构造。
英文摘要：
Uncertainty propagation software can have unknown, inadvertent biases introduced by various means. This work is a case study in bias identification and reduction in one such software package, the Microwave Uncertainty Framework (MUF). The general purpose of the MUF is to provide automated multivariate statistical uncertainty propagation and analysis on a Monte Carlo (MC) basis. Combine is a key module in the MUF, responsible for merging data, raw or transformed, to accurately reflect the variability in the data and in its central tendency. In this work the performance of Combine's MC replicates is analytically compared against its stated design goals. An alternative construction is proposed for Combine's MC replicates and its performance is compared, too, against Combine's design goals. These comparisons are made within an archetypal two-stage scenario in which received data are first transformed in conjunction with shared systematic error and then combined to produce summary information. These comparisons reveal the limited conditions under which Combine's uncertainty results are unbiased and the extent of these biases when these conditions are not met. For small MC sample sizes neither construction, current or alternative, fully meets Combine's design goals, nor does either construction consistently outperform the other. However, for large MC sample sizes the bias in the proposed alternative construction is asymptotically zero, and this construction is recommended.
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。
