The scenario approach is an established data-driven design framework that comes equipped with a powerful theory linking design complexity to generalization properties. In this approach, data are simultaneously used both for design and for certifying the design's reliability, without resorting to a separate test dataset. This paper takes a step further by guaranteeing additional properties, useful in post-design usage but not considered during the design phase. To this end, we introduce a two-level framework of appropriateness: baseline appropriateness, which guides the design process, and post-design appropriateness, which serves as a criterion for a posteriori evaluation. We provide distribution-free upper bounds on the risk of failing to meet the post-design appropriateness; these bounds are computable without using any additional test data. Under additional assumptions, lower bounds are also derived. As part of an effort to demonstrate the usefulness of the proposed methodology, the paper presents two practical examples in H2 and pole-placement problems. Moreover, a method is provided to infer comprehensive distributional knowledge of relevant performance indexes from the available dataset.

翻译：场景方法是一种成熟的数据驱动设计框架，其配备有将设计复杂度与泛化特性相联系的强大理论。在该方法中，数据同时用于设计和验证设计的可靠性，而无需借助单独的测试数据集。本文通过保证在设计阶段未考虑、但在后设计使用中具有实用价值的附加属性，进一步推进了该方法。为此，我们引入了一个双层适用性框架：指导设计过程的基线适用性，以及作为后验评估标准的后设计适用性。我们提供了未能满足后设计适用性风险的分布无关上界；这些界无需使用任何额外测试数据即可计算。在额外假设下，也推导了下界。作为展示所提方法实用性的努力的一部分，本文给出了H2和极点配置问题中的两个实际示例。此外，还提供了一种方法，用于从可用数据集中推断相关性能指标的全面分布知识。