Radek Hofman

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We are concerned with Bayesian identification and prediction of a nonlinear discrete stochastic process. The fact, that a nonlinear process can be approximated by a piecewise linear function advocates the use of adaptive linear models. We propose a linear regression model within a Rao-Blackwellized particle filter. The parameters of the linear model are(More)
Reliable and up to date information represents principal prerequisite for effective management of intervention operations targeted on emergency situations during accidental releases of harmful substances into the atmosphere. Promising way of this trend insists in development of assimilation techniques for improvement of model prognosis reliability on basis(More)
The paper presents a scheme for estimation of spatio–temporal evolution of groundshine dose from radionuclides deposited on terrain in long–time horizon. Groundshine dose mitigation is modeled via semi–empirical formulas taking into account environmental and decay processes. We are aware of the fact that the model is imperfect and special attention is paid(More)
Computational efficiency of the particle filter, as a method based on importance sampling, depends on the choice of the proposal density. Various default schemes, such as the bootstrap proposal, can be very inefficient in demanding applications. Adaptive particle filtering is a general class of algorithms that adapt the proposal function using the observed(More)
17 run-up algorithm. The code requires as input detailed information on seismic source mechanisms, gridded bathymetric data information for the open sea propagation, and a set of gridded Digital Elevation Models (DEM) containing bathymetry and topography for use during the inundation phase. Accurate bathymetry and topography data for Rhodes were(More)
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