模拟计算机模型与高维数计数输出
James M Salter1, Trevelyan J McKinley2, Xiaoyu Xiong1
1Department of Mathematics and Statistics, University of Exeter, Exeter, UK.
概括
这项研究引入了一个新的计算机模型模拟器,用于复杂的模拟. 波桑 (Poisson) 逻辑正常PCA模拟器有效捕获高维数计数数据,这对于流行病学建模至关重要,例如COVID-19传播.
科学领域:
- 计算流行病学计算流行病学
- 统计建模 统计建模
- 不确定性量化不确定性的量化.
背景情况:
- 计算机模型对于现实研究至关重要,但通常是复杂的,昂贵的,需要校准.
- 模拟器作为这些模拟器的经济有效的替代品,在有限的数据上进行训练,以预测不确定性的结果.
- 流行病学模型经常产生高维,空间和时间索引,随机计数数据.
研究的目的:
- 开发和评估Poisson lognormal PCA (PLNPCA) 模拟器,用于来自复杂计算机模型的高维计数数据.
- 将PLNPCA模拟器应用于英格兰和威尔士的COVID-19流行病学模型.
- 将PLNPCA模拟器的性能与在聚合模型输出上训练的模拟器进行比较.
主要方法:
- 在模拟中使用了Poisson lognormal PCA (PLNPCA) 方法.
- 从复杂的计算机模型中对模拟的子集进行模拟器训练.
- 将模拟器应用于来自COVID-19模型的空间和时间索引计数数据.
主要成果:
- 该PLNPCA模拟器表现出与在聚合数据上训练的模拟器相当的性能.
- 模拟器成功捕获了高维输出中的相关性.
- 它提供了计数数据的代表性高维样本,反映了真正的模型输出.
结论:
- PLNPCA模拟器是一个可行的和有效的工具,用于模拟复杂的计算机模型的高维计数输出.
- 这种方法保留了可取的属性,包括预测完整的输出和捕获输出相关性.
- 这种方法特别适用于需要准确量化不确定性的流行病学应用.
相关概念视频
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01:12Modeling and Similitude
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01:24One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation
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01:22Multi-input and Multi-variable systems
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01:14Multicompartment Models: Overview
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01:26Per-Unit Sequence Models
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