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National Repository of Grey Literature

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	Mixing of Predictors in Parameter Estimation Podlesna, Yana ; Kárný, Miroslav This bachelor thesis deals with the design of the method for solving the curse of dimensionality arising in the quantitative modeling of complex interconnected systems. The employed predictive models are based on a discrete Markov process. Prediction is based on estimating model parameters using Bayesian statistics. This work contains method for reducing the amount of data needed for prediction in systems with a large number of occurring states and actions. Instead of estimating a predictor dependent on all parameters, the method assumes the use of several predictors, which arise from estimating parametric models based on dependences on different regressors. The behavioral properties of the proposed method are illustrated by simulation experiments. Detailed record
	Probability Density Estimation Classifier Based on Sample Distances Jiřina, Marcel Fulltext: content.csg - PDF Plný tet: v902-03 - PDF Detailed record
	Error Estimates for Approximate Optimization by the Extended Ritz Method Kůrková, Věra ; Sanguineti, M. Fulltext: content.csg - PDF Plný tet: v882-02 - PDF Detailed record
	All Training Samples Density Estimation Classifier Jiřina, Marcel Fulltext: content.csg - PDF Plný tet: v881-02 - PDF Detailed record
	Tight Bounds on Rates of Variable-Basis Approximation via Estimates of Covering Numbers Kůrková, Věra ; Sanguineti, M. Fulltext: content.csg - PDF Plný tet: v865-02 - PDF Detailed record
	Minimization of Error Functionals over Variable-Basis Functions Kainen, P.C. ; Kůrková, Věra ; Sanguineti, M. Fulltext: content.csg - PDF Plný tet: v864-02 - PDF Detailed record
	Dimension-Independent Approximation by Neural Networks and Its Comparison with Linear Approximation Kůrková, Věra ; Sanguineti, M. Fulltext: content.csg - PDF Plný tet: v789-99 - PDF Detailed record

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