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DEEP CAUSAL LEARNING FOR CONTINUOUS TESTING, DIAGNOSIS, AND OPTIMIZATION

机译:进行深入的因果学习,以进行连续的测试,诊断和优化

摘要

A system and methods for multivariant learning and optimization repeatedly generate self-organized experimental units (SOEUs) based on the one or more assumptions for a randomized multivariate comparison of process decisions to be provided to users of a system. The SOEUs are injected into the system to generate quantified inferences about the process decisions. Responsive to injecting the SOEUs, at least one confidence interval is identified within the quantified inferences, and the SOEUs are iteratively modified based on the at least one confidence interval to identify at least one causal interaction of the process decisions within the system. The causal interaction can be used for testing, diagnosis, and optimization of the system performance.
机译:一种用于多变量学习和优化的系统和方法,基于一个或多个用于向系统用户提供的过程决策的随机多变量比较的假设,反复生成自组织实验单元(SOEU)。 SOEU被注入到系统中以生成有关过程决策的量化推断。响应于注入SOEU,在量化的推论中识别出至少一个置信区间,并且基于至少一个置信区间来迭代地修改SOEU,以识别系统内过程决策的至少一种因果相互作用。因果相互作用可用于测试,诊断和优化系统性能。

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