Software development for Stan to improve survey statistics for non-probability samples

1个项目摘要 这项提案是对美国国立卫生研究院拨款R01 AG067149-01的补充：提高代表性 在非概率调查和因果推断中使用正则化回归和后处理。 该项目涉及开发用于总体抽样调整的某些贝叶斯方法， 灵活、可靠的方法，可用于公共卫生研究中的广泛问题。 该项目需要广泛使用Stan概率编程平台，作为 作为结果方法的一部分，这项研究是正确的。 这个NOSI与那笔赠款是协同的。它将支持新的软件工程计划，以 从三个方面改进核心Stan平台：(1)提供JSON格式输出选项 将提高互操作性并促进将贝叶斯方法融入机器学习 流水线；(2)扩展和重构核心Stan推理算法以获得更大的内存 专用性和增加的并行处理将提高推理的整体速度和可扩展性。 ENCE，允许将贝叶斯方法用于日益复杂的模型。这将允许 研究人员将比较更多和更广泛的模型，以找到那些 最佳行为。(3)添加标准的日志记录框架将使标准 用户社区和开发人员社区。 家长基金的研究议程有三个方面。首先，它旨在解决独特的 公共卫生数据集提出的挑战和通过调查适应现状而提出的问题 艺术造型技法。其次，它努力改进人口学的因果推断 子组。第三，更广泛地说，它寻求通过开发 使用不具代表性的数据来测试和验证模型，以获得更好的 更可信的基于人口的估计。 国家统计局的工作在两个方面都是相关的。首先，它将直接支持在 主要项目。在我们的研究过程中，出现了计算挑战。研究进展 揭示了计算基础设施需要改进的领域；因此，nosi将 使我们能够更有效地开展由美国国立卫生研究院资助的研究。其次，这对结果很重要 我们的研究成果将被其他人使用。Nosi中的计算工作将使它更容易 应用从业者利用我们一直在开发的研究。此外， 增加了用于输入和输出的通用数据格式，提高了处理速度和专用性， 标准化的日志记录将使在复杂的处理管道中使用Stan变得更容易，因此 提高整体云就绪性。

1 Project Summary This proposal is a supplement to our NIH grant R01 AG067149-01: Improving Representativeness in Non-probability Surveys and Causal Inference with Regularized Regression and Poststrati cation. That project involves developing certain Bayesian methods for sampling adjustment in a general, exible, and reliable way that can be used for a wide range of problems in public health research. The project requires extensive use of the Stan probabilistic programming platform, both as part of the research e ort and as part of resulting methods. This NOSI is synergistic with that grant. It will support new software engineering initiatives to improve the core Stan platform in three ways: (1) Providing the option for JSON format outputs will improve interoperability and facilitate incorporating Bayesian methods into machine learning pipelines; (2) Extending and refactoring the core Stan inference algorithms for greater memory eciency and increased parallel processing will improve the overall speed and scalability of infer- ence, allowing for Bayesian methods to be used with increasingly complex models. This will allow researchers to compare a greater number and wider range of models in order to nd those with optimal behaviors. (3) The addition of a standard logging framework will bene t both the Stan user community and the developer community. The parent grant's research agenda is threefold. Firstly, it is directed to addressing the unique challenges posed by public health datasets and questions by investigating adaptations to state-of- the art modelling techniques. Secondly, it strives to improve causal inferences for demographic subgroups. Thirdly, and more broadly, it seeks to improve current methodology by developing work ows to test and validate models with non-representative data in order to obtain better and more trustworthy population based estimates. The work in the NOSI is relevant in two ways. First, it will directly support the research in the main project. During our research, computational challenges arise. The progress in research reveals areas where the computational infrastructure needs to be improved; thus, the NOSI will enable us to do our NIH-funded research more e ectively. Second, it's important for the results of our research to be used by others. The computing work in the NOSI will make it easier for applied practitioners to make use of the research we have been developing. Furthermore, the addition of a common data format for inputs and outputs, greater processing speed and eciency, and standardized logging will make it easier to use Stan in complex processing pipelines, therefore improving overall cloud-readiness.