Piotr Kopka

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—We have applied the methodology combining Bayesian inference with Markov chain Monte Carlo (MCMC) algorithms to the problem of the atmospheric contaminant source localization. The algorithms input data are the on-line arriving information about concentration of given substance registered by sensors' network. A fast-running Gaussian plume dispersion model(More)
—In many areas of application it is important to estimate unknown model parameters in order to model precisely the underlying dynamics of a physical system. In recent years, Sequential Monte Carlo (SMC) methods have become a very popular tool for Bayesian parameter estimation. In this case, the problem of finding the best parameters configuration comes to(More)
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