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.

[1]  Walter R. Gilks,et al.  RESAMPLE-MOVE Filtering with Cross-Model Jumps , 2001, Sequential Monte Carlo Methods in Practice.

[2]  J. Marin,et al.  Population Monte Carlo , 2004 .

[3]  James O. Berger,et al.  A Framework for Validation of Computer Models , 2007, Technometrics.

[4]  K. V. Ramachandra,et al.  Kalman Filtering Techniques for Radar Tracking , 2000 .

[5]  Timothy J. Robinson,et al.  Sequential Monte Carlo Methods in Practice , 2003 .

[6]  D. L. Ermak,et al.  A Real-Time Atmospheric Dispersion Modeling System , 1999 .

[7]  W. Gilks,et al.  Following a moving target—Monte Carlo inference for dynamic Bayesian models , 2001 .

[8]  Jun S. Liu,et al.  Sequential importance sampling for nonparametric Bayes models: The next generation , 1999 .

[9]  David E. Coleman,et al.  Statistical Methods for Detection and Quantification of Environmental Contamination , 2001 .

[10]  Jun S. Liu,et al.  Sequential Monte Carlo methods for dynamic systems , 1997 .

[11]  P. Moral,et al.  Sequential Monte Carlo samplers , 2002, cond-mat/0212648.

[12]  Tim Hesterberg,et al.  Monte Carlo Strategies in Scientific Computing , 2002, Technometrics.

[13]  M. Pitt,et al.  Filtering via Simulation: Auxiliary Particle Filters , 1999 .

[14]  B. Kosović,et al.  Source Inversion for Contaminant Plume Dispersion in Urban Environments Using Building-Resolving Simulations , 2005 .

[15]  Simon J. Godsill,et al.  Improvement Strategies for Monte Carlo Particle Filters , 2001, Sequential Monte Carlo Methods in Practice.

[16]  G Johannesson,et al.  Dynamic Bayesian Models via Monte Carlo - An Introduction with Examples - , 2004 .

[17]  S. T. Chan,et al.  An Evaluation of Two Advanced Turbulence Models for Simulating the Flow and Dispersion Around Buildings , 2004 .

[18]  Radford M. Neal.  Annealed importance sampling , 1998, Stat. Comput..