Advances in Bayesian Nonparametric Methods for Jointly Modeling Multiple Data Sets

贝叶斯非参数是一个统计建模框架，它结合了经典非参数统计方法的灵活性和贝叶斯范式下的不确定性原则评估。然而，传统的贝叶斯非参数方法主要集中在基于单个数据集的模型上，而许多现代统计情景涉及在相关或比较条件下收集的多个类似性质的数据集。该项目开发了一套新的建模和计算策略，用于多个数据集的有效联合建模，其方式为（i）捕获现代复杂数据中的跨样本变化，以及（ii）计算效率高，可以应用于海量数据。开发的方法将在一系列领域产生影响，包括生物学，经济学，教育，天体物理学，政治学和气候科学，其任务是正确表征数据集之间的变化。该项目为研究生提供了极好的研究培训机会。将在两类广泛使用的贝叶斯非参数模型的背景下开发新的模型，方法和算法：（i）具有离散随机测量（DRM）混合分布的混合模型（例如，Dirichlet过程混合物）和（ii）树结构随机测量（TSRM）模型（例如，Polya树型模型）。这两个模型类在本质上是不同的，每个模型类在建模多个数据集时都有自己的优势和局限性，因此推进这两个模型类的策略是不同的。DRM混合物在建模多个样品中的一个关键限制是它们在表征跨样品变化方面缺乏灵活性，因此将开发一种新的潜变量建模策略以大幅提高它们在这方面的能力。还将调查的理论和经验性质的分散混合模型，以及在更广泛的层次模型，其中纳入灵活的跨样本变化的观察和潜在的数量是重要的，这一战略的概括。对于TSRM模型，能够表征复杂的跨样本变化，重点是解决其缺乏可扩展性，计算和统计，相对于增加的维度以及其对底层树结构的敏感性，这是构建此类模型的关键组成部分。这两个模型类的开发将形成一个强大的通用工具箱，可以应用于各种科学和工程问题，包括多个相关数据集的分析。该奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

Bayesian nonparametrics is a statistical modeling framework that combines flexibility of classical nonparametric statistical methods with principled assessment of uncertainty under the Bayesian paradigm. Traditional Bayesian nonparametric methods, however, have largely focused on models based on a single data set, while many modern statistical scenarios involve multiple data sets of similar nature collected under related or comparative conditions. This project develops a suite of new modeling and computational strategies that are tailored for effective joint modeling of multiple data sets in ways that (i) capture cross-sample variation in modern complex data, and (ii) are computationally efficient to allow application to massive data. The developed methodology will have impact in a range of fields including biology, economics, education, astrophysics, political science, and climate science, where the task is to properly characterize variation across data sets. The project provides excellent research training opportunities for graduate students.Novel models, methods, and algorithms will be developed in the context of two classes of widely used Bayesian nonparametric models: (i) mixture models with discrete random measure (DRM) mixing distributions (e.g., Dirichlet process mixtures) and (ii) tree-structured random measure (TSRM) models (e.g., Polya tree type models). These two model classes are different in nature with each having its own advantages and limitations in modeling multiple data sets, and as such the strategies in advancing these two model classes are distinct. A key limitation of DRM mixtures in modeling multiple samples is their lack of flexibility in characterizing the cross-sample variation, and thus a new latent variable modeling strategy will be developed for substantially enhancing their capacity in this regard. Also to be investigated are the theoretical and empirical properties of the resulting dispersion mixture models, as well as generalizations of this strategy in broader ranges of hierarchical models where incorporating flexible cross-sample variation in observed and latent quantities is important. For TSRM models, capable of characterizing complex cross-sample variation, the focus is on addressing their lack of scalability, both computational and statistical, with respect to increasing dimensionality as well as their sensitivity to the underlying tree structures, a critical component for building such models. The development for these two model classes will form a powerful and general toolbox that can be applied in a variety of scientific and engineering problems involving the analysis of multiple related data sets.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.