一个通用的模型验证和测试工具
A General Model Validation and Testing Tool
摘要：
我们构建并提出了“贝叶斯验证度量”（BVM）作为一个通用的模型验证和测试工具。我们发现BVM能够代表所有的标准验证指标（平方误差，可靠性，协议的概率，频率，面积，概率密度比较，统计假设检验，贝叶斯模型检验）作为特殊情况下，并发现它可以用来改善，概括，或进一步量化其不确定性。因此，BVM允许我们以新的方式评估现有验证指标之间的相似性和差异性。BVM有能力允许用户根据新的验证要求发明和选择模型。我们制定并测试了一些新的化合物验证指标，改善了文献中的其他验证指标。此外，我们构建BVM比率的目的是在存在或不存在不确定性的情况下，根据用户定义的一致性定义量化模型选择。这种构造概括了贝叶斯模型测试框架。
英文摘要：
We construct and propose the "Bayesian Validation Metric" (BVM) as a general model validation and testing tool. We find the BVM to be capable of representing all of the standard validation metrics (square error, reliability, probability of agreement, frequentist, area, probability density comparison, statistical hypothesis testing, and Bayesian model testing) as special cases and find that it can be used to improve, generalize, or further quantify their uncertainties. Thus, the BVM allows us to assess the similarities and differences between existing validation metrics in a new light. The BVM has the capacity to allow users to invent and select models according to novel validation requirements. We formulate and test a few novel compound validation metrics that improve upon other validation metrics in the literature. Further, we construct the BVM Ratio for the purpose of quantifying model selection under user defined definitions of agreement in the presence or absence of uncertainty. This construction generalizes the Bayesian model testing framework.
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。
