Sequential Monte-Carlo Based Framework for Dynamic Data-Driven Event Reconstruction for Atmospheric Release
Conference
·
OSTI ID:883520
Atmospheric releases of hazardous materials are highly effective means to impact large 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.
- Research Organization:
- Lawrence Livermore National Laboratory (LLNL), Livermore, CA
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- W-7405-ENG-48
- OSTI ID:
- 883520
- Report Number(s):
- UCRL-PROC-217190
- Country of Publication:
- United States
- Language:
- English
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