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Implementation and Application of Statistical Methods in Research, Manufacturing Technology and Quality Control
Kupka, Karel ; Karpíšek, Zdeněk (advisor)
This thesis deals with modern statistical approaches and their application aimed at robust methods and neural network modelling. Selected methods are analyzed and applied on frequent practical problems in czech industry and technology. Topics and methods are to be benificial in real applications compared to currently used classical methods. Applicability and effectivity of the algorithms is verified and demonstrated on real studies and problems in czech industrial and research bodies. The great and unexploited potential of modern theoretical and computational capacity and the potential of new approaces to statistical modelling and methods. A significant result of this thesis is also an environment for software application development for data analysis with own programming language DARWin (Data Analysis Robot for Windows) for implemenation of effective numerical algorithms for extaction information from data. The thesis should be an incentive for boarder use of robust and computationally intensive methods as neural networks for modelling processes, quality control and generally better understanding of nature.
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Finite Mixture Models
Rusý, Tomáš ; Komárek, Arnošt (advisor) ; Omelka, Marek (referee)
This work focuses on finite mixture models and aims to introduce the maximum likelihood method as an approach of fitting finite mixtures. For that purpose the EM algorithm is adopted and derived in detail. Both the E and M steps of the EM algorithm are presented and performed for general finite mixture model. We derive new estimates for some of parameters defining the model. All updated estimates of the iteration of the EM algorithm are derived explicitly for the specific family of normal mixtures. The described theory is then applied to a model of the production of cytokine interleukin 10 in human periodontitis attack, which clearly demonstrates an application of the model in practice. Finally, we discuss the theory of clustering, which is based on our previous results. Like the previous theory, this one is also illustrated in the aforementioned model of cytokine IL10 production. Powered by TCPDF (www.tcpdf.org)
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Implementation and Application of Statistical Methods in Research, Manufacturing Technology and Quality Control
Kupka, Karel ; Karpíšek, Zdeněk (advisor)
This thesis deals with modern statistical approaches and their application aimed at robust methods and neural network modelling. Selected methods are analyzed and applied on frequent practical problems in czech industry and technology. Topics and methods are to be benificial in real applications compared to currently used classical methods. Applicability and effectivity of the algorithms is verified and demonstrated on real studies and problems in czech industrial and research bodies. The great and unexploited potential of modern theoretical and computational capacity and the potential of new approaces to statistical modelling and methods. A significant result of this thesis is also an environment for software application development for data analysis with own programming language DARWin (Data Analysis Robot for Windows) for implemenation of effective numerical algorithms for extaction information from data. The thesis should be an incentive for boarder use of robust and computationally intensive methods as neural networks for modelling processes, quality control and generally better understanding of nature.
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Implementation and Application of Statistical Methods in Research, Manufacturing Technology and Quality Control
Kupka, Karel ; Šeda, Miloš (referee) ; Militký, Jiří (referee) ; Dohnal, Gejza (referee) ; Karpíšek, Zdeněk (advisor)
This thesis deals with modern statistical approaches and their application aimed at robust methods and neural network modelling. Selected methods are analyzed and applied on frequent practical problems in czech industry and technology. Topics and methods are to be benificial in real applications compared to currently used classical methods. Applicability and effectivity of the algorithms is verified and demonstrated on real studies and problems in czech industrial and research bodies. The great and unexploited potential of modern theoretical and computational capacity and the potential of new approaces to statistical modelling and methods. A significant result of this thesis is also an environment for software application development for data analysis with own programming language DARWin (Data Analysis Robot for Windows) for implemenation of effective numerical algorithms for extaction information from data. The thesis should be an incentive for boarder use of robust and computationally intensive methods as neural networks for modelling processes, quality control and generally better understanding of nature.
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