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.

有许多应用程序的兴趣是从观察到的数据中了解未知数。目标范围从预测未来数据到了解科学或社会真理。贝叶斯信号处理允许明确纳入所有可用的信息有关一个解决的任务。它相当于将常识知识和观察证据最佳地结合起来。由于其强大的力量和吸引力，贝叶斯建模和分析已被所有科学和工程所接受。然而，当前的主要问题是那些具有大量未知数（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，贝叶斯推理可能会成为计算无法处理它们，因为纯粹的规模和复杂性的研究系统。本项目的目标是推进贝叶斯方法的一个类别的理论和实践，自适应重要性抽样（AIS），处理问题的未知数和/或数据的数量是大的。本项目的重点是建立一个新的框架，AIS将扩展其使用贝叶斯推理的问题与大量的未知数和/或数据。该研究涉及最详细地调查AIS在几个领域的复杂性，包括（a）AIS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）应用于其中对从癌症干细胞理解癌症进展感兴趣的病例研究。