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.

在许多应用中，人们感兴趣的是从观察数据中了解未知。目标范围从预测未来的数据到了解科学或社会真相。贝叶斯信号处理允许显式合并关于被寻址任务的所有可用信息。它相当于将常识知识和观察证据完美地结合在一起。贝叶斯建模与分析以其强大的生命力和感染力，受到了科学界和工程界的广泛欢迎。然而，当前的主要问题是那些具有大量未知因素(复杂系统)和/或大量数据(大数据)的问题。这引发了人们的担忧，即由于所研究系统的巨大规模和复杂性，贝叶斯推理可能在计算上无法处理它们。本项目的目标是促进一类贝叶斯方法的理论和实践，即自适应重要性抽样(AIS)，用于处理未知和/或数据数量较大的问题。本项目致力于构建一个新的AIS框架，将其用于贝叶斯推理扩展到具有大量未知和/或数据的问题。这项研究涉及到在几个领域最详细地调查人工智能的复杂性，包括(A)人工智能的新方案，重点是自适应学习和稳定权重计算的新策略，以及处理高维模型和大数据的先进方法，(B)模型选择和机器学习，(C)全局优化，以及(D)在案例研究中的应用，其中理解癌症干细胞的癌症进展是令人感兴趣的。