CIF: Small: Advancing Adaptive Importance Sampling for Signal Processing

CIF：小型：推进信号处理的自适应重要性采样

• 批准号: 1617986
• 负责人: Monica Bugallo
• 金额: $ 49.85万
• 依托单位: SUNY at Stony Brook
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-06-15 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1617986&HistoricalAwards=false
• 关键词: CIF; Small; Advancing; Adaptive; Importance

There are many applications where the interest is in learning about unknowns from observed data. The goals range from predicting future data to learning about scientific or societal truths. Bayesian signal processing allows for explicit incorporation of all available information about an addressed task. It amounts to optimally combining common-sense knowledge and observational evidence. Due to its strength and appeal, Bayesian modeling and analysis has been embraced by all of science and engineering. However, the main current problems are those with large numbers of unknowns (complex systems) and/or large amounts of data (big data). This raises the concern that Bayesian inference may become computationally incapable of handling them because of the sheer size and complexity of the studied systems. The goal of this project is to advance the theory and practice of a class of Bayesian methods, adaptive importance sampling (AIS), for dealing with problems where the numbers of unknowns and/or data are large.This project focuses on building a novel framework for AIS that will extend its use for Bayesian inference to problems with large amounts of unknowns and/or data. The research involves investigating in greatest detail the intricacies of AIS on several areas including (a) novel schemes for AIS with emphasis on new strategies for adaptive learning and for stable weight computation, and on advanced approaches for dealing with high dimensional models and big data, (b) model selection and machine learning, (c) global optimization, and (d) application to a case-study where understanding the progression of cancer from cancer stem cells is of interest.

在许多应用程序中，从观察到的数据中了解未知数的兴趣。目标范围从预测未来数据到学习科学或社会真理。贝叶斯信号处理允许明确合并有关寻址任务的所有可用信息。这相当于最佳地结合常识性知识和观察证据。由于其力量和吸引力，贝叶斯建模和分析已被所有科学和工程所接受。但是，当前的主要问题是那些有大量未知数（复杂系统）和/或大量数据（大数据）的问题。这引起了人们的关注，即贝叶斯推论可能在计算上无法处理它们，因为研究系统的规模和复杂性的纯粹和复杂性。该项目的目的是推进一类贝叶斯方法的理论和实践，适应性重要性采样（AIS），以处理不明数据和/或数据的数量很大的问题。本项目着重于建立AIS的新型框架，以扩展其对贝叶斯对贝叶斯对大量不知名和/或/或/或/或/或/或/或/或/或/或/或/或/或/或/或/或/或/或/或数据的问题进行扩展的使用。这项研究涉及对AIS对多个领域的复杂性进行的研究，包括（a）AIS的新方案，重点是用于适应性学习和稳定的体重计算的新策略，以及用于处理高维模型和大数据和大数据的先进方法，（B）造成癌症的癌症，（c）对癌症进行癌症的兴趣。