Complexity to Clarity: Nonparametric Procedures that Exploit Structured Data and Models

从复杂到清晰：利用结构化数据和模型的非参数过程

• 批准号: 1521786
• 负责人: Ann Lee
• 金额: $ 45万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1521786&HistoricalAwards=false
• 关键词: Complexity; Clarity; Nonparametric; Procedures; Exploit

Modern research in the physical sciences involves the use of non-standard aggregate data objects (for example, images, spectra, or hurricane tracks), which are not directly amenable to traditional statistical methods. Naturally, the physical processes that generated these observed data are also often very complicated. Typically the only meaningful "model" is in the form of a high-resolution theoretical or simulation model. For example in cosmology, scientists regularly use large hydrodynamic simulations to understand how the universe formed and evolved. The goal of this project is to develop a new means of combining careful statistical modeling of scientific phenomena with scalable procedures that fully exploit the richness of large collections of complex data without reducing the data to a set of features or templates.Building on ideas from harmonic analysis and spectral methods, this project is to develop flexible and adaptive nonparametric methods for high-dimensional inference that exploit sparse (and potentially nonlinear) structure in complex data. These methods derive Fourier-like bases that adapt to the intrinsic geometry (e.g., submanifold structure) of the underlying data distribution, and use the empirical basis functions to estimate functions on high-dimensional aggregate objects. The methods go beyond point-estimates in prediction to nonparametric estimation of conditional densities, density ratios and likelihoods of complex, high-dimensional data. A key application of these methods is the calibration of complex simulation models: the inference challenge of determining the settings of input values to these models so that their output approximates either real data or the output of a more complex and computationally-intensive simulation code. On a broader scale, this work will make key methodological contributions to building, interpreting, and using probability models for high-dimensional, complexly-structured data in a wide range of scientific applications.

物理科学的现代研究涉及使用非标准的汇总数据对象（例如，图像、光谱或飓风轨迹），这些对象不能直接适用于传统的统计方法。自然，产生这些观测数据的物理过程也常常非常复杂。通常，唯一有意义的“模型”是以高分辨率理论或模拟模型的形式存在的。例如，在宇宙学中，科学家经常使用大型流体动力学模拟来了解宇宙是如何形成和演化的。该项目的目标是开发一种新的方法，将科学现象的仔细统计建模与可扩展的程序相结合，充分利用大量复杂数据的丰富性，而不将数据减少到一组特征或模板。基于谐波分析和频谱方法的思想，该项目旨在开发灵活和自适应的非参数方法，用于高维推理，利用复杂数据中的稀疏（和潜在的非线性）结构。这些方法推导出适合底层数据分布的固有几何（如子流形结构）的类傅里叶基，并使用经验基函数来估计高维聚合对象上的函数。该方法超越了预测中的点估计，到复杂高维数据的条件密度、密度比和可能性的非参数估计。这些方法的一个关键应用是复杂模拟模型的校准：确定这些模型的输入值设置的推理挑战，以便它们的输出接近真实数据或更复杂和计算密集的模拟代码的输出。在更广泛的范围内，这项工作将为在广泛的科学应用中建立、解释和使用高维、复杂结构数据的概率模型做出关键的方法论贡献。