Inference for Stationary Processes: Optimal Transport and Generalized Bayesian Approaches

平稳过程的推理：最优传输和广义贝叶斯方法

• 批准号: 2113676
• 负责人: Andrew Nobel
• 金额: $ 29.99万
• 依托单位: University of North Carolina at Chapel Hill
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2113676&HistoricalAwards=false
• 关键词: Inference; Stationary; Processes; Optimal; Transport

This project will address the problem of making inferences about sequences of observations that exhibit dependence arising from physical or other interactions. Observations of this sort occur in many fields, including finance, ecology, natural language processing, and biology. We will explore ways to fit a sequence of observations to a family of statistical models using ideas from the theory of optimal transport. Informally, we will identify models in the family into which the generating mechanism of the observations can be transformed with the least overall cost. We will address both the theory and efficient computation of these transformation costs, and will consider applications to biomedicine and computer science. The project will involve collaborations with graduate students and more senior researchers working in genomics and bioinformatics. Both undergraduate and graduate students will receive training through involvement in supported research projects.On a more technical level, this project will address inference for stochastic processes, in particular, how to fit a family of stationary processes to an observed ergodic process, revealed sequentially. Research will focus on the use and extension of ideas from optimal transport, including stationary couplings of stationary processes and related variational quantities, with a focus on methods development and supporting theory. The research has two primary aims. The first aim is to investigate minimum divergence estimation based on joinings, including the use and properties of entropy regularization. The second aim is to investigate the efficient computation of optimal transition couplings of Markov chains, with applications to graph distances, graph alignment, and hidden Markov models. The project will involve collaborations with graduate students and more senior researchers working in genomics and bioinformatics. Both undergraduate and graduate students will receive training through involvement in supported research projects.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.

这个项目将解决的问题作出推论的观察序列，表现出依赖性所产生的物理或其他相互作用。 这类观察发生在许多领域，包括金融、生态学、自然语言处理和生物学。 我们将探索如何使用最优传输理论的思想将一系列观测值拟合到一系列统计模型中。 非正式地，我们将确定家庭中的模型，其中观测的生成机制可以以最小的总成本进行转换。 我们将解决这些转换成本的理论和有效计算，并将考虑应用到生物医学和计算机科学。该项目将涉及与研究生和更高级的研究人员在基因组学和生物信息学工作的合作。 本科生和研究生都将通过参与支持的研究项目接受培训。在更技术的层面上，该项目将解决随机过程的推理，特别是如何将一个平稳过程族拟合到一个观察到的遍历过程中，依次揭示。研究将集中在使用和扩展的想法，从最佳运输，包括固定过程和相关的变量的固定耦合，重点是方法的开发和支持理论。 这项研究有两个主要目的。 第一个目标是研究基于连接的最小散度估计，包括熵正则化的使用和性质。 第二个目标是研究马尔可夫链的最优转移耦合的有效计算，并应用于图距离，图对齐和隐马尔可夫模型。 该项目将涉及与研究生和更高级的研究人员在基因组学和生物信息学工作的合作。 本科生和研究生都将通过参与支持的研究项目接受培训。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。