Collabrative Research: Sequential Monte Carlo Methods and Their Applications

协作研究：序贯蒙特卡罗方法及其应用

• 批准号: 0073651
• 负责人: Xiaodong Wang
• 金额: $ 19.51万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-09-15 至 2002-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0073651&HistoricalAwards=false
• 关键词: Collabrative; Research; Sequential; Monte; Carlo

Collaborative Research: Sequential Monte Carlo Methods and Their ApplicationsJun Liu, Harvard University Rong Chen, Univ. Illinois at ChicagoXiaodong Wang, Texas A&M UniversityAbstract (Technical):Sequential Monte Carlo (SMC) methodology recently emerged in statistics and engineering fields promises to solve a wide class of nonlinear filtering and optimization problems, opening up new frontiers for cross-fertilization between statistics and many areas of applications. A distinctive feature of SMC is its ability to adapt to the dynamics of the underlying stochastic systems via recursive simulation of the variables involved. Although special forms of SMC date back to 1950s, the general use of the method appeared only recently and its many key properties have yet been well understood. This research group will focus on three major theoretical issues regarding the design of effective SMC-based computational tools and three important application areas, namely, wireless communications, computational biology, and business data analysis. In the theory part, they will study approaches of generating better Monte Carlo samples for tracking system dynamics; investigate roles of resampling which is critical to the effectiveness of SMC; and propose system reconfiguration strategies for more efficient SMC algorithms. In the application part, they plan to design novel signal processing and network control algorithms for wireless multimedia communications; develop better multiple sequence alignment models and SMC-based optimization method for protein structures; and build SMC-based modeling and analysis tools for business data. It is anticipated that the proposed research will culminate in the formulation of novel SMC methodologies and will bring the promise of the SMC paradigm into the practical arena of many emerging applications.Stochastic dynamic systems are routinely used in many application fields such as automatic control, engineering, and finance. The statistical analyses of these systems are crucial. However, except for a few special cases, quantitative analyses of these systems still present major challenges to researchers. Sequential Monte Carlo (SMC) technique recently emerged in the field of statistics and engineering shows a great promise on solving a wide class of nonlinear filtering, prediction, and optimization problems, providing us with many exciting new research opportunities. The name "Monte Carlo" was coined in 1940s by scientists involved in designing atomic bombs and it refers to a technique in which computer is used to simulate and study a complex stochastic system. The technique was named after the famed gambling resort because its procedures incorporate the element of chance. A distinctive feature of SMC is its ability to sequentially simulate the system by considering one variable at a time. The general use of SMC appeared recently and its invasion into many fields of science and engineering has just begun. Researchers including people in this research group have demonstrated that SMC can be successfully adapted to solve chemistry, engineering, and statistical problems. Understanding its theoretical properties and extending the use of SMC to other fields are the main focuses of this project. More specifically, this research group will focuse on three major theoretical issues regarding the design of effective SMC-based computational tools and three important application areas including wireless communications, computational biology, and business data analysis. These applications are not only important by their own merits, but also essential as the test ground for the new theories being developed and as the sources of stimulation for new research directions for SMC. It is anticipated that this research will culminate in the formulation of novel SMC methodologies and will bring the promise of the SMC paradigm into the practical arena of many emerging applications. In particular, this research will bear fruits in the following areas: novel designs of signal processing and network control algorithms for wireless multimedia communications; developments of better algorithms analyzing biological sequence and structure data; and a SMC-based tool for business data analysis and prediction.

合作研究：序贯蒙特卡罗方法及其应用Jun Liu，哈佛大学Rong Chen，伊利诺伊大学芝加哥分校Xiaodong Wang，德克萨斯州A& M大学摘要（技术）：序贯蒙特卡罗（SMC）方法最近出现在统计和工程领域，有望解决广泛的非线性滤波和优化问题，为统计和许多应用领域之间的交叉施肥开辟了新的前沿。SMC的一个显着特点是它能够通过递归模拟所涉及的变量来适应底层随机系统的动态。 虽然SMC的特殊形式可以追溯到20世纪50年代，但该方法的普遍使用直到最近才出现，并且其许多关键特性尚未得到很好的理解。 该研究小组将专注于设计有效的基于SMC的计算工具的三个主要理论问题和三个重要应用领域，即无线通信，计算生物学和商业数据分析。 在理论部分，他们将研究生成更好的蒙特卡罗样本跟踪系统动态的方法;研究对SMC有效性至关重要的重新配置的作用;并提出更有效的SMC算法的系统重新配置策略。 在应用部分，他们计划设计新的无线多媒体通信信号处理和网络控制算法;开发更好的多序列比对模型和基于SMC的蛋白质结构优化方法;并建立基于SMC的业务数据建模和分析工具。预计，拟议的研究将最终在制定新的SMC方法，并将带来的承诺SMC范式到实际的竞技场的许多新兴的application.Stochastic动态系统经常使用在许多应用领域，如自动控制，工程和金融。 对这些系统的统计分析至关重要。 然而，除了少数特殊情况下，这些系统的定量分析仍然是研究人员面临的主要挑战。 序列蒙特卡罗（SMC）技术是近年来在统计和工程领域中出现的一种新技术，它在解决非线性滤波、预测和优化等问题方面有着广阔的应用前景，为我们提供了许多新的研究机会。 蒙特卡洛这个名字是在20世纪40年代由参与设计原子弹的科学家创造的，它指的是一种用计算机模拟和研究复杂随机系统的技术。这项技术以著名的赌博胜地命名，因为它的程序包含了机会的元素。SMC的一个显着特点是它能够通过一次考虑一个变量来顺序模拟系统。 SMC的广泛应用是最近才出现的，它对科学和工程的许多领域的入侵才刚刚开始。包括该研究小组成员在内的研究人员已经证明，SMC可以成功地解决化学、工程和统计问题。了解其理论特性并将SMC的应用扩展到其他领域是本项目的主要重点。更具体地说，这个研究小组将集中在三个主要的理论问题，关于设计有效的基于SMC的计算工具和三个重要的应用领域，包括无线通信，计算生物学和商业数据分析。这些应用程序不仅是重要的，因为它们本身的优点，但也是必不可少的，作为新的理论正在开发的测试场，并作为SMC的新的研究方向的刺激源。 预计这项研究将最终在制定新的SMC方法，并将带来的承诺SMC范式到许多新兴应用的实际竞技场。 特别是，这项研究将在以下领域取得成果：无线多媒体通信的信号处理和网络控制算法的新设计;更好的算法分析生物序列和结构数据的发展;和基于SMC的商业数据分析和预测工具。