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

1项目概要 此提案是对我们的NIH资助R 01 AG 067149 -01的补充：提高代表性 在非概率调查和因果推理与正则化回归和后分层。 该项目涉及开发某些贝叶斯方法，用于一般的抽样调整， 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， 该研究是作为结果方法的一部分。 这个NOSI是协同与赠款。它将支持新的软件工程计划， 从三个方面改进Stan核心平台：（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纳入机器学习 （2）扩展和重构核心Stan推理算法以增加内存 效率和增加的并行处理将提高推理的整体速度和可扩展性， 这使得贝叶斯方法可以用于越来越复杂的模型。这将允许 研究人员比较更多的和更广泛的模型，以发现那些与 最佳行为(3)标准日志框架的添加将贝内标准 用户社区和开发者社区。 家长补助金的研究议程有三个方面。首先，它旨在解决独特的 公共卫生数据集带来的挑战和通过调查适应现状的问题 艺术造型技术。其次，它致力于改善人口统计学的因果推断， 分组。第三，更广泛地说，它力求通过发展 工作流测试和验证模型与非代表性的数据，以获得更好的， 更可靠的人口估计。 NOSI的工作在两个方面具有相关性。首先，它将直接支持研究， 的主要项目。在我们的研究过程中，出现了计算挑战。的研究进展 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进行NIH资助的研究。其次，这对结果很重要 我们的研究成果被其他人利用NOSI中的计算工作将使 应用实践者利用我们一直在开发的研究。而且 增加了输入和输出的通用数据格式，提高了处理速度和效率， 标准化的日志记录将使Stan更容易在复杂的处理管道中使用， 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.