Estimating Source Term Parameters through Probabilistic Bayesian inference: An Approach based on an Adaptive Multiple Importance Sampling Algorithm

Abstract : This paper presents an adaptive approach based on probabilistic Bayesian inference to estimate the parameters of an atmospheric pollution source term. After introducing the problem and assessing the computational framework, we present an Importance Sampling based algorithm called Adaptive Multiple Importance Sampling (AMIS). It performs an efficient calculation of the source parameter posterior distribution by iteratively upgrading the proposal's parameters and recycling all generations of weighted samples, thus allowing a faster convergence and reducing the number of necessary iterations. We highlight the results of the AMIS by comparing it to a MCMC estimation in a simple example.
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https://hal-imt.archives-ouvertes.fr/hal-01064661
Contributor : François Septier <>
Submitted on : Tuesday, September 16, 2014 - 5:40:23 PM
Last modification on : Thursday, October 17, 2019 - 12:35:47 PM

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  • HAL Id : hal-01064661, version 1

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Harizo Rajaona, Patrick Armand, François Septier, Yves Delignon, Christophe Olry, et al.. Estimating Source Term Parameters through Probabilistic Bayesian inference: An Approach based on an Adaptive Multiple Importance Sampling Algorithm. 16th International Conference on Harmonisation within Atmospheric Dispersion Modelling for Regulatory Purposes (HARMO 16), Sep 2014, Varna, Bulgaria. pp.1-5. ⟨hal-01064661⟩

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