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Statistical inference and uncertainty quantification for complex process-based models using multiple data sets

使用多个数据集对基于过程的复杂模型进行统计推断和不确定性量化

基本信息

批准号：
NE/T00973X/1
负责人：
Richard Everitt
金额：
$ 38.53万
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=NE%2FT00973X%2F1
关键词：
Statistical inference uncertainty quantification complex

项目摘要

Making responsible decisions about landscapes is facilitated by the use of complex models able to represent multiple competing demands on land use. Decisions about land use require that trade-offs between competing demands be identified, and their consequences through time be characterised. Methods for representing consequences through time on maps generally take the form of complex models such as stochastic computer simulations. Such models are increasingly used to make realistic predictions about real world processes from socio-ecological systems involving land use to the effects of climate change. Because these models attempt to simulate all relevant aspects of a real physical system, they may involve many parameters, some of which will be difficult to set correctly. As the final objective of these models is to assess the possible consequences of management decisions, such as the placement of wind turbines, it is crucially important that the uncertainty introduced by calibrating parameters be understood.Approximate Bayesian Computation, or ABC, is a promising technique for estimating parameter values together with their credible intervals, and this allows calculation of the uncertainty deriving from parameter calibration. The overarching aim of this proposal is to improve ABC, or related approaches, to make them sufficiently fast and accurate that they can be widely used for the evaluation and calibration of complex stochastic computer models, and to quantify the uncertainty attached to their predictions. This process is complicated by the fact that making decisions about land use involves taking into account multiple processes and multiple datasets: this proposal aims to develop methods specifically designed for this situation.The end goal of the project is to be able to fit and evaluate the accuracy of complex models for real, challenging applications, and for this approach to be more widely used in practice. We will work with investigators in the landscape decision-making programme, and others involved in landscape decision modelling, to apply the methods we develop to their models. Our proposal develops and brings to bear cutting-edge mathematical and statistical methodologies to calibrate complex models, and to quantify the uncertainty in their predictions that derives from parameter calibration.

通过使用能够代表对土地使用的多种竞争需求的复杂模型，可以促进对景观做出负责任的决策。关于土地使用的决定要求确定相互竞争的需求之间的权衡，并描述其随时间的后果。在地图上表示随时间变化的后果的方法通常采用复杂模型的形式，例如随机计算机模拟。这些模型越来越多地用于对真实的世界进程作出现实的预测，从涉及土地使用的社会生态系统到气候变化的影响。由于这些模型试图模拟真实的物理系统的所有相关方面，因此它们可能涉及许多参数，其中一些参数将难以正确设置。由于这些模型的最终目标是评估管理决策的可能后果，如风力涡轮机的位置，这是至关重要的是，引入校准参数的不确定性被understood.Approximate Bayesian Computation，或ABC，是一种很有前途的技术，估计参数值连同其可信区间，这使得计算的不确定性来自参数校准。该提案的首要目标是改进ABC或相关方法，使其足够快速和准确，从而可广泛用于评估和校准复杂的随机计算机模型，并量化其预测的不确定性。由于土地利用决策涉及多个过程和多个数据集，这一过程变得复杂：本项目旨在开发专门针对这种情况设计的方法。该项目的最终目标是能够拟合和评估复杂模型的准确性，以适应真实的、具有挑战性的应用，并使这种方法在实践中得到更广泛的应用。我们将与景观决策计划的调查人员以及其他参与景观决策建模的人员合作，将我们开发的方法应用于他们的模型。我们的建议开发并带来了尖端的数学和统计方法来校准复杂的模型，并量化其预测中的不确定性，这些不确定性来自参数校准。