ABI Development: An extensible software platform for integrating multiple sources of data and uncertainty using hierarchical statistical models

ABI 开发：一个可扩展的软件平台，用于使用分层统计模型集成多个数据源和不确定性

• 批准号: 1147230
• 负责人: Perry de Valpine
• 金额: $ 91.29万
• 依托单位: University of California-Berkeley
• 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 2017-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1147230&HistoricalAwards=false
• 关键词: ABI; Development; extensible; software; platform

Hierarchical statistical models allow estimation of patterns in complex biological data while accounting for relationships such as temporal or spatial patterns or shared sampling units. A great variety of analysis algorithms for hierarchical models have been developed by statistical researchers but are unavailable to practitioners such as experimental or field biologists. These include many types of Markov chain Monte Carlo, as well as sequential Monte Carlo, importance sampling, approximate Bayesian computation, and other numerical methods and approximations. In addition, there are many higher-level algorithms that use these as components of methods for model selection, model averaging, maximum likelihood estimation, generating predictions, and more. This project will involve development of an open source, extensible software environment for flexible composition of hierarchical models and algorithms. The software will include low-level components in which algorithms will be executed for speed, high-level components in which algorithms can be composed and managed from the R statistical software environment, and middle-level components to interface the first two. Many algorithms will be implemented and disseminated for application using the new software. Moreover, it will provide a foundation for ongoing development and sharing of new and improved algorithms in the future.Hierarchical statistical models are used in many domains of biology to provide robust conclusions and management guidance that harness all available data. Areas of application include wildlife conservation and management, ecosystem processes such as carbon cycling, organismal growth and development, and cellular biochemical networks. In all of these areas, biologists need to use complicated data to estimate the processes and rates of change occurring in their study system. This project will provide a next generation of software to make available numerous algorithms to many researchers to achieve this goal. These algorithms will facilitate research workflows by allowing researchers to extract the most information from their data in an efficient manner.

分层统计模型允许在复杂的生物数据中估计模式，同时考虑诸如时间或空间模式或共享采样单元之类的关系。 统计学研究人员已经开发了大量的层次模型的分析算法，但对于实验或现场生物学家等从业者来说是不可用的。 这些包括许多类型的马尔可夫链蒙特卡罗，以及顺序蒙特卡罗，重要性抽样，近似贝叶斯计算和其他数值方法和近似。 此外，还有许多更高级别的算法将这些算法用作模型选择、模型平均、最大似然估计、生成预测等方法的组成部分。 该项目将涉及开发一个开放源代码、可扩展的软件环境，用于灵活组合分层模型和算法。 该软件将包括低级组件，其中将执行算法以提高速度，高级组件，其中可以从R统计软件环境中组成和管理算法，以及中间级组件，以连接前两个组件。 许多算法将使用新的软件加以实施和推广应用。 此外，它将为未来持续开发和共享新的和改进的算法提供基础。分层统计模型用于生物学的许多领域，以提供利用所有可用数据的稳健结论和管理指南。 应用领域包括野生动物保护和管理，生态系统过程，如碳循环，有机体生长和发育，以及细胞生物化学网络。 在所有这些领域中，生物学家需要使用复杂的数据来估计其研究系统中发生的变化过程和速度。 该项目将提供下一代软件，为许多研究人员提供许多算法来实现这一目标。 这些算法将通过允许研究人员以有效的方式从数据中提取最多的信息来促进研究工作流程。