Bayesian likelihood-free localisation of a biochemical source using multiple dispersion models

Localisation of a source of a toxic release of biochemical aerosols in the atmosphere is a problem of great importance for public safety. Two main practical difficulties are encountered in this problem: the lack of knowledge of the likelihood function of measurements collected by biochemical sensors, and the plethora of candidate dispersion models, developed under various assumptions (e.g. meteorological conditions, terrain). Aiming to overcome these two difficulties, the paper proposes a likelihood-free approximate Bayesian computation method, which simultaneously uses a set of candidate dispersion models, to localise the source. This estimation framework is implemented via the Monte Carlo method and tested using two experimental datasets. HighlightsWe develop a statistical method for adaptive likelihood free Bayesian estimation and model selection.The method is developed in the context of localisation of an emitting source of toxic material in the atmosphere.Three atmospheric dispersion models are considered.Two real datasets are used to assess the performance of the proposed method.

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