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
基本信息
- 批准号:1205516
- 负责人:
- 金额:$ 49.96万
- 依托单位:
- 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:
- 关键词:
项目摘要
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,编码,文档,测试,传播和维护Stan,这是一个可扩展的开源软件框架和编译器,用于高效和可扩展的贝叶斯统计建模。Stan是一个可扩展的,开源的,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的兴趣量建立一个全概率模型。Stan通过提供一种用于指定概率模型的表达性和可扩展的特定于领域的编程语言来促进这一过程。通过将模型规范编译成可执行代码,Stan完全自动化了贝叶斯推理的第二步,即计算未观测到的数量的概率,例如模型参数和未来观测,以观测数据为条件。第三步涉及评估模型对数据的拟合及其对未观测数据的预测。当模型易于编码且推理快速且自动计算时,它可以很容易地迭代规范,拟合和评估步骤,以细化科学模型。Stan在算法和实现细节方面都改进了现有的技术水平。Stan模型不像它的前辈那样被动态地解释,而是被编译成C++代码,这极大地提高了可伸缩性和效率。Stan为统计建模所需的函数提供了一个完整的算法微分库。这种方法将微积分中的链式法则应用到计算概率函数的程序中,以便有效而准确地计算导数(计算函数所需时间的小倍数,与维数无关)。这使得Stan可以完全自动化模型拟合阶段,只需在Stan的建模语言中指定概率函数。为了最大限度地提高Stan对科学界的可访问性,它使用符合标准的C++进行编码,以便它可以在Windows,Macintosh和Unix/Linux下运行。为了使运行Stan更容易,它可以从R,MATLAB和Python调用,这三个最流行的数值分析平台,包括探索和绘图。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Andrew Gelman其他文献
C3(H<sub>2</sub>O) – Generation, quantitation, and marker of human disease
- DOI:
10.1016/j.molimm.2018.06.058 - 发表时间:
2018-10-01 - 期刊:
- 影响因子:
- 作者:
Michelle Elvington;M. Kathryn Liszewski;Hrishikesh Kulkarni;Andrew Gelman;Alfred Kim;John Atkinson - 通讯作者:
John Atkinson
A default prior distribution for logistic and other regression models ∗
逻辑和其他回归模型的默认先验分布 *
- DOI:
- 发表时间:
2002 - 期刊:
- 影响因子:0
- 作者:
Andrew Gelman;Aleks Jakulin;M. G. Pittau;Yu - 通讯作者:
Yu
An improved BISG for inferring race from surname and geolocation
一种改进的 BISG,用于根据姓氏和地理位置推断种族
- DOI:
10.48550/arxiv.2310.15097 - 发表时间:
2023 - 期刊:
- 影响因子:0
- 作者:
P. Greengard;Andrew Gelman - 通讯作者:
Andrew Gelman
Community prevalence of SARS-CoV-2 in England during April to September 2020: Results from the ONS Coronavirus Infection Survey
2020 年 4 月至 9 月英格兰 SARS-CoV-2 社区流行情况:ONS 冠状病毒感染调查结果
- DOI:
10.1101/2020.10.26.20219428 - 发表时间:
2020 - 期刊:
- 影响因子:0
- 作者:
K. Pouwels;T. House;E. Pritchard;J. Robotham;Paul J. Birrell;Andrew Gelman;K. Vihta;N. Bowers;Ian Boreham;Heledd Thomas;James W Lewis;Iain Bell;J. Bell;J. Newton;J. Farrar;I. Diamond;P. Benton;A. Walker - 通讯作者:
A. Walker
Ethics and Statistics: It's Too Hard to Publish Criticisms and Obtain Data for Republication
伦理与统计学:发表批评和获取重发表数据太难了
- DOI:
- 发表时间:
2013 - 期刊:
- 影响因子:0
- 作者:
Andrew Gelman - 通讯作者:
Andrew Gelman
Andrew Gelman的其他文献
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{{ truncateString('Andrew Gelman', 18)}}的其他基金
Scalable Bayesian regression: Analytical and numerical tools for efficient Bayesian analysis in the large data regime
可扩展贝叶斯回归:在大数据领域进行高效贝叶斯分析的分析和数值工具
- 批准号:
2311354 - 财政年份:2023
- 资助金额:
$ 49.96万 - 项目类别:
Standard Grant
RAPID: Flexible, Efficient, and Available Bayesian Computation for Epidemic Models
RAPID:灵活、高效、可用的流行病模型贝叶斯计算
- 批准号:
2055251 - 财政年份:2020
- 资助金额:
$ 49.96万 - 项目类别:
Standard Grant
Collaborative Research: PPoSS: Planning: Scalable Systems for Probabilistic Programming
协作研究:PPoSS:规划:概率编程的可扩展系统
- 批准号:
2029022 - 财政年份:2020
- 资助金额:
$ 49.96万 - 项目类别:
Standard Grant
RIDIR: Collaborative Research: Bayesian analytical tools to improve survey estimates for subpopulations and small areas
RIDIR:协作研究:贝叶斯分析工具,用于改进亚人群和小区域的调查估计
- 批准号:
1926578 - 财政年份:2019
- 资助金额:
$ 49.96万 - 项目类别:
Standard Grant
CI-SUSTAIN: Stan for the Long Run
CI-SUSTAIN:长远发展
- 批准号:
1730414 - 财政年份:2017
- 资助金额:
$ 49.96万 - 项目类别:
Standard Grant
Collaborative Research: Multilevel Regression and Poststratification: A Unified Framework for Survey Weighted Inference
协作研究:多级回归和后分层:调查加权推理的统一框架
- 批准号:
1534414 - 财政年份:2015
- 资助金额:
$ 49.96万 - 项目类别:
Standard Grant
CMG: Reconstructing Climate from Tree Ring Data
CMG:从树木年轮数据重建气候
- 批准号:
0934516 - 财政年份:2009
- 资助金额:
$ 49.96万 - 项目类别:
Standard Grant
Design and Analysis of "How many X's do you know" surveys for the study of polarization in social networks
用于研究社交网络极化的“你知道多少个 X”调查的设计和分析
- 批准号:
0532231 - 财政年份:2005
- 资助金额:
$ 49.96万 - 项目类别:
Standard Grant
Multilevel Modeling for the Study of Public Opinion and Voting
用于民意和投票研究的多层次建模
- 批准号:
0318115 - 财政年份:2003
- 资助金额:
$ 49.96万 - 项目类别:
Continuing Grant
Doctoral Dissertation Research: Estimating Congressional District-Level Opinions from National Surveys using a Bayesian Hierarchical Logistic Regression Model
博士论文研究:使用贝叶斯分层逻辑回归模型从全国调查中估计国会选区级意见
- 批准号:
0241709 - 财政年份:2003
- 资助金额:
$ 49.96万 - 项目类别:
Standard Grant
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