Anna Wawrzynczak

Learn More
—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)
—We have applied the methodology combining Bayesian inference with Genetic algorithm (GA) 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. To achieve rapid-response event reconstructions the fast-running(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)
  • 1