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Robust, Scalable Sequential Monte Carlo with Application To Urban Air Quality

稳健、可扩展的顺序蒙特卡罗在城市空气质量中的应用

基本信息

批准号：
EP/T004134/1
负责人：
Adam Johansen
金额：
$ 79.25万
依托单位：
University of Warwick
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2020
资助国家：
英国
起止时间：
2020 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FT004134%2F1
关键词：
Robust Scalable Sequential Monte Carlo

项目摘要

This project is driven by two substantial considerations.Methods for conducting inference, i.e. estimating the parameters of an indirectly observed system, in large complex systems are urgently needed. Existing technology does not generally scale well to the very large data sets which arise in many modern data-rich contexts. Most of the recent developments in computational statistics which aim at improving the scalability of existing algorithms have focused on data which has very particular forms and in particular can be viewed as very large numbers of replicates of measurements which are independent of one another. Such methods are not suitable for data sets which have strong spatial and temporal structures as, for example, many data sets obtained in urban analytic settings do. This project aims to develop a suite of methodological tools for conducting inference in models of this sort in a computationally efficient way, by exploiting the structure of the models in order to provide simultaneously efficient computational tools and good estimation. Furthermore, leveraging recent developments in the field of robust statistics, these methods will be adapted to deal with settings in which the modelling is imperfect and the data generating process is not exactly characterized by the mathematical model. This robustness is essential to obtain good performance in real, complex scenarios.Air quality monitoring is a tremendously important and tremendously challenging area. Diverse sensor networks exist on different scales and provide measurements with quite different characteristics to one another. Fusing this information as observations become available is a large scale statistical inference problem. Indeed, problems of this type motivate the methodological development of this project and will serve as an extensive test-bed for the developed methodology. An extended application of those methods to air quality monitoring in the Greater London area with the support of the Greater London Authority provides the second major aspect of this proposal.

这个项目是由两个实质性的考虑推动的：迫切需要在大型复杂系统中进行推理的方法，即估计间接观测系统的参数。现有技术通常不能很好地扩展到在许多现代数据丰富的环境中出现的非常大的数据集。计算统计学的大多数最新发展旨在提高现有算法的可伸缩性，主要集中在具有非常特殊形式的数据，特别是可以被视为彼此独立的非常大量的测量重复。这种方法不适合于具有强烈的时空结构的数据集，例如，在城市分析环境中获得的许多数据集就是这样。该项目旨在开发一套方法学工具，通过利用模型的结构，以计算高效的方式在这类模型中进行推理，以便同时提供高效的计算工具和良好的估计。此外，利用稳健统计领域的最新发展，这些方法将被调整，以处理建模不完善和数据生成过程不完全由数学模型描述的情况。要在真实、复杂的场景中获得良好的性能，这种稳健性是必不可少的。空气质量监测是一个非常重要且具有极大挑战性的领域。不同的传感器网络存在于不同的尺度上，并提供彼此具有完全不同特征的测量结果。随着观测结果的出现，融合这些信息是一个大规模的统计推断问题。事实上，这类问题推动了这个项目的方法论发展，并将成为发展方法论的广泛试验台。在大伦敦管理局的支持下，将这些方法扩大到大伦敦地区的空气质量监测，是这项提议的第二个主要方面。