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Národní úložiště šedé literatury Nalezeno 3 záznamů.  Hledání trvalo 0.00 vteřin. 
Notes on projection based modelling of beta-distributed weights of a two-component mixture
 Dedecius, Kamil
This report contains brief notes on estimation of beta-distributed weight of a Gaussian mixture. The results are directly applied in paper Kárný, M.: On approximate Bayesian recursive estimation]. First, we develop a method to update the beta distribution of weights by new data (evidences) and show, that a projection is needed to preserve the low modelling complexity. Then, we show how forgetting may be applied to improve adaptivity. The results can be immediately applied to multicomponent mixtures.
Úplný záznam
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.
Úplný záznam
Impact of forgetting on models of rolling mills
 Dedecius, Kamil ; Jirsa, Ladislav
The research report deals with an analysis of various models for modelling of the cold sheet rolling process. It comprises a thorough analysis of a mass-flow model and its weaknesses, brief analysis of normalization impact on modelling and exhaustive analysis of 4 defined models with exponential and partial forgetting and their comparison to models without forgetting. The report ends with a computer-intensive search for new blackbox models.
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