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EM algorithm
Vacula, Ondřej ; Komárek, Arnošt (advisor) ; Antoch, Jaromír (referee)
This paper discusses the EM algorithm. This algorithm is used, for example, to calculate maximum likelihood estimate of unknown parameter. The algorithm is based on repeated calculations of certain expected value and maximizing specific function. We begin with parameter estimation problem, describe the maximum likelihood method and concept of incomplete data. Then we formulate the EM algorithm and its properties. In the next chapter we apply this knowledge to three selected statistical problems. At first we examine standard mixture model, then the linear mixed model and finally we analyze censored data. Powered by TCPDF (www.tcpdf.org)
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Dřevostavba penzionu v Bělé pod Pradědem
Vacula, Ondřej
The bachelor thesis deals with the design of a wooden house in Bělá pod Pradědem. The guesthouse is designed as a diffuse-open frame construction with panel mounting. In the first part it deals with information about wood, wooden constructions, accommodation legislation and guesthouse classification. The next part is devoted to methodology, which contains a selection of suitable variant of location on the land and design of the guesthouse with respect to standards and surrounding area. The last part describes the selected design, disposition, design of vertical supporting structures, choice of materials, transportation of panels to the building site and equipment of the guesthouse. The work is supplemented with drawing documentation and technical report.
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EM algorithm
Vacula, Ondřej ; Komárek, Arnošt (advisor) ; Antoch, Jaromír (referee)
This paper discusses the EM algorithm. This algorithm is used, for example, to calculate maximum likelihood estimate of unknown parameter. The algorithm is based on repeated calculations of certain expected value and maximizing specific function. We begin with parameter estimation problem, describe the maximum likelihood method and concept of incomplete data. Then we formulate the EM algorithm and its properties. In the next chapter we apply this knowledge to three selected statistical problems. At first we examine standard mixture model, then the linear mixed model and finally we analyze censored data. Powered by TCPDF (www.tcpdf.org)
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