A Bayesian inference scheme is used to address the problem of general source reconstruction. To this purpose, starting from a simple, physically motivated structure–based representation for a general source distribution, Bayesian probability theory is used to formulate the full joint posterior probability density function for the parameters in this representation. The exploration of the high–dimensional parameter space using simulated annealing and a Markov chain Monte Carlo sampler is described. The explicit linkage between the computational tools used for posterior sampling and concepts in statistical mechanics is emphasised. The proposed methodology is validated using a real dispersion experiment involving a multiple source release.
Keywords: Bayesian inference, Markov chain, Monte Carlo simulation, inverse dispersion, data assimilation, source reconstruction, statistical mechanics, simulated annealing, dispersion modelling, air pollution, atmospheric dispersion