CI-ADDO-NEW: Stan, Scalable Software for Bayesian Modeling

CI-ADDO-NEW：Stan，用于贝叶斯建模的可扩展软件

• 批准号: 1205516
• 负责人: Andrew Gelman
• 金额: $ 49.96万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-06-01 至 2015-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1205516&HistoricalAwards=false
• 关键词: CI; ADDO; NEW; Stan; Scalable

This award is to design, code, document, test, dissememinate, and maintain Stan,an extensibleopen-source software framework and compiler for efficient and scalable Bayesian statistical modeling.Stan is an extensible, open-source, cross-platform software framework for developing Bayesian statisticalmodels. The first step in Bayesian modeling is setting up a full probability model for all quantities ofinterest. Stan facilitates this process by providing an expressive and extensible domain-specificprogramming language for specifying probabilistic models. By compiling a model specification intoexecutable code, Stan fully automates the second step of Bayesian inference, calculating the probabilitiesof unobserved quantities, such as model parameters and future observations, conditional on observed data.The third step involves evaluating the fit of the model to the data and its predictions for unseen data.When the model is easy to encode and inferences are fast and automatic to compute, it is easy to iteratethe specification, fit and evaluation steps in order to refine the scientific model.Stan improves on the existing state of the art in both algorithmic and implementation details. Rather thanbeing interpreted on the fly like its predecessors, Stan models are compiled to C++ code, whichdramatically improves both scalability and efficiency. Stan provides a full algorithmic differentiation library for the functions required for statistical modeling. This method applies the chain rule from calculus to the program computing the probability function in order to calculate derivatives efficiently and accurately (a small multiple of the time taken to compute thefunction, independently of dimensionality). This allows Stan to fully automate the model fitting stagegiven only a specification of the probability function in Stan's modeling language.To maximize Stan's accessibility to the scientific community, it is being coded using standards-compliantC++, so that it will run under Windows, Macintosh, and Unix/Linux. To make running Stan even easier,it is callable from R, MATLAB, and Python, the three most popular platforms for numerical analysis,including exploration and plotting.

该奖项旨在设计，代码，文档，测试，传播和维护Stan，这是一个扩展的托管软件框架和编译器，用于高效且可扩展的贝叶斯统计建模。STAN是一种可扩展的开放源，跨平台的软件框架，用于开发贝叶斯统计模型。贝叶斯建模的第一步是为所有款式的所有数量建立一个完整的概率模型。 Stan通过为指定概率模型提供一种表达和可扩展的域特异性程序语言来促进此过程。通过将模型规范汇编为可取消的代码，Stan完全自动化了贝叶斯推理的第二步，计算未观察到的数量的概率，例如模型参数和未来的观察结果，以观察到的数据为条件。第三步涉及评估模型的拟合度及其对未透露式数据的拟合度的拟合度。为了完善科学模型，伊特拉特（Iterate）的规范，适合和评估步骤。斯坦（Stan）在算法和实施细节中都改善了现有的最新技术状态。 Stan模型并没有像其前身一样随时解释，而是将C ++代码汇编为C ++代码，从而提高了可扩展性和效率。 Stan为统计建模所需的功能提供了完整的算法分化库。此方法将链条规则从微积分到程序计算概率函数以有效，准确地计算衍生物（在计算功能所花费的时间的一小倍数（独立于维度）所花费的时间）。这使Stan仅在Stan的建模语言中仅对概率函数的规范完全自动化模型拟合。为了最大程度地提高Stan对科学界的可访问性，它正在使用符合标准的+++进行编码，以便它在Windows，Macintosh和Unix/linux下运行。为了使运行Stan更加容易，它可以从R，Matlab和Python中呼唤，这是三个最受欢迎的数值分析平台，包括探索和绘图。