CAREER: Sequential Monte Carlo Methods for High Dimensional Systems

职业：高维系统的序贯蒙特卡罗方法

• 批准号: 0953316
• 负责人: Monica Bugallo
• 金额: $ 39.97万
• 依托单位: SUNY at Stony Brook
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-01 至 2016-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0953316&HistoricalAwards=false
• 关键词: CAREER; Sequential; Monte; Carlo; Methods

CAREER: Sequential Monte Carlo Methods for High Dimensional SystemsAbstractAdvances in the development of models and methods that can satisfactorily describe and analyze high dimensional systems are extremely valuable for many different disciplines including biology, meteorology, economics,social sciences, and engineering. These systems are characterized by nonlinearities and are difficult to understand.Computational methods governed by simple local rules have the potential of providing insightful interpretationsand of paving the way towards quantitative and qualitative descriptions and understanding of complex systems.This project is focused on development of such methods and in particular on the development of a synergistic research and educational program in sequential Monte Carlo-based signal processing for high dimensional systems.This research aims at laying the foundations of a sequential Monte Carlo methodology for high dimensional systems. In the literature, there are claims stating that particle filters cannot be used for complex systems because their random measures degenerate to single particles. While this is true for standard implementation of these filters, it does not hold true for alternative approaches. A new methodology based on the principle of divide and conquer is developed. In particular, the collapse of traditional particle filltering is avoided by setting an interconnected network of filters, each of them working on lower dimensional spaces. Research tasks include development of the theoretical grounds of the methods, establishment of guidelines for its use by practitioners,analysis of its accuracy, stability and scalability, and validation on a wide range of complex systems. The proposed methods are original and provide solutions to arguably the biggest open problem of particle filtering.

职业：在模型和方法的开发中，可以令人满意地描述和分析高维系统的模型和方法中的高维系统吸引力的顺序蒙特卡洛方法对于许多不同的学科都非常有价值，这对于包括生物学，气象学，经济学，经济学，社会科学和工程的许多不同学科非常有价值。这些系统的特征是非线性的，并且很难理解。Compationationalcontipational propational concontiational progation tim Prace规则的潜力有可能提供洞察力的解释，并为定量和定性描述和对复杂系统的理解铺平道路的方式。该项目集中于此类方法的开发，尤其是针对层次研究和层次的跨度研究的开发，以进行层次的跨度研究。高维系统的顺序蒙特卡洛方法的基础。在文献中，有声称指出粒子过滤器不能用于复杂系统，因为它们的随机测量值将其退化为单个颗粒。尽管对于这些过滤器的标准实施是正确的，但对于替代方法并不成立。开发了一种基于鸿沟和征服原则的新方法。特别是，通过设置互连的过滤器网络来避免传统粒子填充的崩溃，每个网络都在较低的空间上工作。研究任务包括开发方法的理论基础，为从业人员使用其使用指南，分析其准确性，稳定性和可扩展性以及对广泛复杂系统的验证。所提出的方法是原始的，可以说是粒子过滤的最大开放问题。