大气释放动态数据驱动事件重构的顺序蒙特卡罗框架

2006 IEEE Nonlinear Statistical Signal Processing Workshop Pub Date : 2005-11-16 DOI:10.1109/NSSPW.2006.4378840

G. Jóhannesson, K. Dyer, W. Hanley, B. Kosović, S. Larsen, G. Loosmore, J. Lundquist, A. Mirin

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引用次数: 16

摘要

向大气中释放有害物质会对人口稠密的地区产生巨大影响。我们提出了一个大气事件重建框架，该框架通过贝叶斯方法将观测数据和预测计算机密集色散模型耦合在一起。由于模型框架的复杂性，采用了一种基于抽样的后验推理方法，将马尔可夫链蒙特卡罗(MCMC)和序列蒙特卡罗(SMC)策略相结合。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Sequential Monte-Carlo Framework for Dynamic Data-Driven Event Reconstruction for Atmospheric Release

The release of hazardous materials into the atmosphere can have a tremendous impact on dense populations. We propose an atmospheric event reconstruction framework that couples observed data and predictive computer-intensive dispersion models via Bayesian methodology. Due to the complexity of the model framework, a sampling-based approach is taken for posterior inference that combines Markov chain Monte Carlo (MCMC) and sequential Monte Carlo (SMC) strategies.

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2006 IEEE Nonlinear Statistical Signal Processing Workshop

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