CI-SUSTAIN: Stan for the Long Run

CI-SUSTAIN：长远发展

• 批准号: 1730414
• 负责人: Andrew Gelman
• 金额: $ 98.39万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-01 至 2020-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1730414&HistoricalAwards=false
• 关键词: CI; SUSTAIN; Stan; Long; Run

Stan is a software package that transforms scientific discovery by allowing scientists to quickly and easily explore, evaluate, and refine rich scientific hypotheses tailored to their particular research question and data collection mechanism. For computational reasons, analyses of data (big or otherwise) have tended to be simple and focused more on the difficulties of manipulating the data than on realistic scientific models. The next generation of Bayesian inference can take scientists beyond this impasse, via sophisticated models that can adjust for differences between sample and population, and between treatment and control groups, to join the benefits of large datasets with the rigor and power of statistical adjustment. Stan further helps educate the next generation of data scientists, with a natural, easy-to-learn and portable modeling language, coupled with robust, practical inference tools. The specific goal of this project is to solidify the Stan code base to enable application, maintenance, and development of the Stan software. Stan is being applied in many corners of the physical, biological, and social sciences, hundreds of at scales ranging from the neutrinos to supernovas, from cellular biology to population ecology, and from human reaction times to social network evolution. In this project the PI aims to document and ruggedize the core infrastructure of Stan to enable it to be used by a wider audience of scientists, to be maintained by a wider group of software developers, and to be extensible to allow for the future development of new scientific applications and statistical algorithms.Technically, this project is devoted to thoroughly documenting for users and developers, testing at the unit, integration, and functional levels, and inevitably refactoring the application programming interfaces (API) of Stan's components. These components include (1) an automatically differentiable mathematics, statistics, and matrix algebra library, (2) an imperative probabilistic programming language for expressing statistical data/parameter structures and scientific/measurement models, (3) core inference algorithms for providing exact and approximate full Bayesian parameter estimation and predictive inference, (4) a service layer of high-level commands, I/O callbacks, and interrupts, and (5) interfaces integrating Stan's probability modeling, analysis and visualization capabilities into existing data science workflows in languages including R, Python, Julia, Stata, MATLAB, Mathematica and the command-line for cloud and cluster computing. The main goal of the refactoring is to achieve enough modularity in system components and documentation that developers will be able to concentrate on a single component, such as a new function, algorithm, or visualization, as well as to allow Stan to be used as a research tool for algorithm development. These goals complement the goals of documenting the language and interfaces in order to promote rigorous statistical methodology and reproducible computational workflows for applied scientists.

STAN是一个软件包，通过允许科学家快速、轻松地探索、评估和完善针对他们特定的研究问题和数据收集机制的丰富的科学假设，来转变科学发现。由于计算方面的原因，对数据的分析(无论是大的还是其他的)往往都很简单，更多地关注操纵数据的困难，而不是现实的科学模型。下一代贝叶斯推理可以带领科学家走出这一僵局，通过复杂的模型来调整样本和总体之间的差异，以及治疗组和对照组之间的差异，将大数据集的好处与统计调整的精确度和能力结合起来。斯坦通过一种自然、易于学习和可移植的建模语言，加上强大、实用的推理工具，进一步帮助培养下一代数据科学家。该项目的具体目标是巩固STAN代码库，以实现STAN软件的应用、维护和开发。斯坦被应用于物理、生物和社会科学的许多角落，从中微子到超新星，从细胞生物学到种群生态学，从人类反应时间到社会网络进化，数以百计的人都在使用斯坦。在这个项目中，PI旨在记录和加固STAN的核心基础设施，使其能够被更广泛的科学家使用，由更广泛的软件开发人员维护，并具有可扩展性，以允许未来开发新的科学应用程序和统计算法。在技术上，该项目致力于为用户和开发人员彻底记录文档，在单元、集成和功能级别进行测试，并不可避免地重构STAN组件的应用程序编程接口(API)。这些组件包括(1)自动可微的数学、统计和矩阵代数库，(2)用于表示统计数据/参数结构和科学/测量模型的命令性概率编程语言，(3)用于提供准确和近似的完整贝叶斯参数估计和预测推理的核心推理算法，(4)高级命令、I/O回调和中断的服务层，以及(5)将Stan的概率建模、分析和可视化能力集成到现有数据科学工作流中的接口，其语言包括R、Python、Julia、Stata、matlab和用于云和集群计算的命令行。重构的主要目标是在系统组件和文档中实现足够的模块化，以便开发人员能够专注于单个组件，例如新函数、算法或可视化，并允许将Stan用作算法开发的研究工具。这些目标补充了记录语言和界面的目标，以便为应用科学家促进严格的统计方法和可重复使用的计算工作流程。

项目成果

Using Stacking to Average Bayesian Predictive Distributions (with Discussion)

• DOI: 10.1214/17-ba1091
• 发表时间: 2018-09-01
• 期刊: BAYESIAN ANALYSIS
• 影响因子: 4.4
• 作者: Yao, Yuling;Vehtari, Aki;Tonellato, Stefano
• 通讯作者: Tonellato, Stefano

R-squared for Bayesian Regression Models

• DOI: 10.1080/00031305.2018.1549100
• 发表时间: 2019-07-03
• 期刊: AMERICAN STATISTICIAN
• 影响因子: 1.8
• 作者: Gelman, Andrew;Goodrich, Ben;Vehtari, Aki
• 通讯作者: Vehtari, Aki

Visualization in Bayesian workflow

• DOI: 10.1111/rssa.12378
• 发表时间: 2019-02-01
• 期刊: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES A-STATISTICS IN SOCIETY
• 影响因子: 2
• 作者: Gabry, Jonah;Simpson, Daniel;Gelman, Andrew
• 通讯作者: Gelman, Andrew

BAYESIAN AGGREGATION OF AVERAGE DATA: AN APPLICATION IN DRUG DEVELOPMENT

• DOI: 10.1214/17-aoas1122
• 发表时间: 2018-09-01
• 期刊: ANNALS OF APPLIED STATISTICS
• 影响因子: 1.8
• 作者: Weber, Sebastian;Gelman, Andrew;Racine-Poon, Amy
• 通讯作者: Racine-Poon, Amy