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贝叶斯灵敏度和不确定性分析在碳动力学计算机模拟机统计分析中的应用
Applications of Bayesian Sensitivity and Uncertainty Analysis to the Statistical Analysis of Computer Simulators for Carbon Dynamics |
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| 课程网址: |
http://videolectures.net/mlws04_kennedy_absua/
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| 主讲教师: |
Marc Kennedy
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| 开课单位: |
谢菲尔德大学
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| 开课时间: |
2007-02-25
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| 课程语种: |
英语
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| 中文简介: |
关于森林生态系统中碳的动态变化的不确定性对于确定和核实政策具有重大影响，从批准“京都议定书”的困难可以看出这一点。量化和减少这种不确定性需要在统一的统计框架内结合生态过程的数学模型和地球观测数据。陆地碳动力学中心（CTCD）正在开发若干计算机代码，以模拟不同空间和时间尺度的相关过程。在给定地点对这些代码的输入描述了那里生长的植被的特征。土壤和气候数据也用于驱动模型。我的演讲将说明在开发这些代码时使用高效的贝叶斯工具以及用于预测和减少不确定性的方法。第一步是构建计算机代码的模拟器。仿真器是基于高斯过程先验模型的代码输出的统计表示。由此我们可以得出关于一系列灵敏度和不确定性度量的推论：执行灵敏度分析以找出每个输入或输入组对输出的影响程度。这可以通过揭示非活动输入来提高效率。根据各个输入检查输出的预期响应曲线也发现了许多编码错误。不确定性分析用于评估由各种不确定输入条件导致的预测的不确定性。它还告诉我们，如果我们想减少输出中的总不确定性，在哪里集中研究工作来减少投入的不确定性。传统的灵敏度分析和不确定性分析方法涉及代码输出的蒙特卡罗采样。这是非常低效的，并且对于复杂模型是不可行的。贝叶斯方法可以将所需的模拟器运行次数减少几个数量级。我还将提到正在开发的方法的一些扩展，以处理CTCD植被模型的动态和多变量性质。
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| 课程简介: |
Uncertainties about the dynamics of carbon in forest ecosystems have a major impact on defining and verifying policies, as is evident from the difficulties in ratifying the Kyoto protocol. Quantifying and reducing this uncertainty requires the combination of mathematical models for ecological processes, and earth observation data, within a unifying statistical framework. The Centre for Terrestrial Carbon Dynamics (CTCD) is developing several computer codes to simulate the relevant processes at different spatial and temporal scales. Inputs to theses codes at a given site describe the characteristics of the vegetation grown there. Soil and climate data are also used to drive the model. My talk will illustrate the use of efficient Bayesian tools both in the development of these codes and in their use for prediction and uncertainty reduction. The first step is to build an emulator of the computer code. The emulator is a statistical representation of the code output based on a Gaussian process prior model. From this we can derive inferences about a range of sensitivity and uncertainty measures: Sensitivity analysis is performed to find out the level of influence each input or group of inputs have on the output. This can lead to efficiency gains by revealing inactive inputs. Examination of the expected response curve of the output as a function of individual inputs has also uncovered a number of coding errors. Uncertainty analysis is employed to assess the uncertainty in the prediction resulting from the various uncertain input conditions. It also tells us where to concentrate research effort in reducing uncertainties in inputs if we want to reduce the total uncertainty in the output. Conventional approaches to sensitivity analysis and uncertainty analysis involve Monte Carlo sampling of code outputs. This is highly inefficient and is not feasible for complex models. Bayesian methods can reduce the required number of simulator runs by several orders of magnitude. I will also mention some extensions to the methodology that are being developed to handle the dynamic and multivariate nature of the CTCD vegetation models.
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| 关 键 词: |
森林生态系统; 碳; 贝叶斯工具
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| 课程来源: |
视频讲座网
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| 最后编审: |
2019-09-05：lxf
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| 阅读次数: |
31
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