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Daugiamačių Gauso skirstinių mišinio statistinė analizė, taikant duomenų projektavimą / The Projection-based Statistical Analysis of the Multivariate Gaussian Distribution Mixture

Problem of the dissertation. The Gaussian random values are very common in practice, because if a random value depends on many additive factors, according to the Central Limit Theorem (if particular conditions are satisfied), the sum is approximately from Gaussian distribution. If the observed random value belongs to one of the several classes, it is from the Gaussian distribution mixture model. The mixtures of the Gaussian distributions are common in various fields: biology, medicine, astronomy, military science and many others. The most important statistical problems are problems of mixture identification and data clustering. In case of high data dimension, these tasks are not completely solved. The new parameter estimation of the multivariate Gaussian distribution mixture model and data clustering methods are proposed and analysed in the dissertation. Since it is much easier to solve these problems in univariate case, the projection-based approach is used. The aim of the dissertation. The aim of this work is the development of constructive algorithms for distribution analysis and clustering of data from the mixture model of the Gaussian distributions.

Identiferoai:union.ndltd.org:LABT_ETD/oai:elaba.lt:LT-eLABa-0001:E.02~2005~D_20050121_131502-50982
Date21 January 2005
CreatorsKavaliauskas, Mindaugas
ContributorsČekanavičius, Vydas, Miškinis, Paulius, Račkauskas, Alfredas, Januškevičius, Romanas, Radavičius, Marijus, Dučinskas, Kęstutis, Kubilius, Kęstutis, Vilnius Gediminas Technical University
PublisherLithuanian Academic Libraries Network (LABT), Vilnius Gediminas Technical University
Source SetsLithuanian ETD submission system
LanguageLithuanian
Detected LanguageEnglish
TypeDoctoral thesis
Formatapplication/pdf
Sourcehttp://vddb.library.lt/obj/LT-eLABa-0001:E.02~2005~D_20050121_131502-50982
RightsUnrestricted

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