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 is(More)
PURPOSE The objective of the study was to evaluate the efficacy of ciprofloxacin prophylaxis for patients undergoing high-dose chemotherapy followed by autologous stem cell transplantation (ASCT). MATERIALS AND METHODS The data of 104 patients transplanted at the Department of Hematology Medical University of Lodz between 2005 and 2008 were analyzed. The(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 this context the Bayesian approach is a powerful tool to combine observed data along with prior knowledge to gain a current (probabilistic) understanding of unknown model parameters. We have applied(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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