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Modelling of Traffic Flow with Bayesian Autoregressive Model with Variable Partial Forgetting
Dedecius, Kamil ; Nagy, Ivan ; Hofman, Radek
Computing the future road traffic intensities in urban and suburban areas is considered inthis paper. The statistical properties of the traffic flow advocate the use of a low-order lin- ear autoregressive models, in which the previous intensities determine the following ones. To achieve adaptivity, the Bayesian modelling framework was chosen. The regression coefficients are considered random, hence they are modelled using a suitable distribution. A significant improvement of the overall modelling performance is further reached with techniques allowing the parameters vary by modification of their distribution. We present the partial forgetting method, allowing to individually track the parameters even in the case of their different variability rate.
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Asimilace časoprostorového rozložení radionuklidů v časné fázi radiační nehody
Hofman, Radek ; Šmídl, Václav
Exploitation of the data assimilation methodology in the early phase of radiation accident is studied. When radioactive pollutants are released into the atmosphere, a radioactive plume is passing over the terrain. The released radioactive material causes pathway-specific irradiation which has detrimental effects on population health. In order to ensure efficiency of introduced countermeasures, it is necessary to predict spatial and temporal distribution of the aerial pollution and material already deposited on the ground. The predictions are made by the means of a numerical dispersion model with many inputs. Output of such a model is a prediction of radiation situation given in terms of radiological quantities. Exact values of the inputs are uncertain due to the stochastic nature of the dispersion, lack of accurate information, etc. Their subjective choice can introduce significant errors into the predictions and thus decrease the positive impact of the countermeasures.
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Aplikace opomíjeného praktického filtru pro lineárně Gausovské problémy s neznámým modelem chyb kovariantních struktur
Hofman, Radek
The paper presents a scheme for estimation of spatio-temporal evolution of a quantity with unknown model error. Model error is estimated on basis of measured-minus-observed residuals evaluated upon measured and modeled values. Methods of Bayesian filtering are applied to the problem. The main contribution of this paper is application of general marginalized particle filter algorithm to the linear-Gaussian problem with unknown model error covariance structure. Methodology is demonstrated on the problem of modeling of spatio-temporal evolution of groundshine-dose from radionuclides deposited on terrain in long-time horizon.
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Aplikace pokročilých statistických metod v odhadu pozdních fázý nukleárních nehod
Hofman, Radek
The paper presents a new methodology for improving of estimates of radiological situation on terrain in the late phase of a nuclear accident. Methods of Bayesian filtering are applied to the problem. The estimates are based on combination of modeled and measured data provided by responsible authorities. Exploiting information on uncertainty of both the data sources, we are able to produce improved estimate of the true situation on terrain. We also attempt to account for model error, which is unknown and plays crucial role in accuracy of the estimates. The main contribution of this paper is application of an approach based on advanced statistical methods, which allows for estimating of model error covariance structure upon measurements. Model error is estimated on basis of measured-minus-observed residuals evaluated upon measured and modeled values. The methodology is demonstrated on a sample scenario with simulated measurements.
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