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010   $a3-319-64671-0
024 7 $a10.1007/978-3-319-64671-8
035   $a(CKB)3710000001631071
035   $a(MiAaPQ)EBC4978947
035   $a(DE-He213)978-3-319-64671-8
035   $a(PPN)203851714
035   $a(EXLCZ)993710000001631071
100   $a20170816d2017      u|         0
101 0 $aeng
135   $aurcnu||||||||
181   $2rdacontent
182   $2rdamedia
183   $2rdacarrier
200 10$aAlgorithms and programs of dynamic mixture estimation $eunified approach to different types of components /$fby Ivan Nagy, Evgenia Suzdaleva
205   $a1st ed. 2017.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2017.
215   $a1 online resource (113 pages) $cillustrations, tables
225 1 $aSpringerBriefs in Statistics,$x2191-544X
311   $a3-319-64670-2 
320   $aIncludes bibliographical references.
327   $aIntroduction -- Basic Models -- Statistical Analysis of Dynamic Mixtures -- Dynamic Mixture Estimation -- Program Codes -- Experiments -- Appendices.
330   $aThis book provides a general theoretical background for constructing the recursive Bayesian estimation algorithms for mixture models. It collects the recursive algorithms for estimating dynamic mixtures of various distributions and brings them in the unified form, providing a scheme for constructing the estimation algorithm for a mixture of components modeled by distributions with reproducible statistics. It offers the recursive estimation of dynamic mixtures, which are free of iterative processes and close to analytical solutions as much as possible. In addition, these methods can be used online and simultaneously perform learning, which improves their efficiency during estimation. The book includes detailed program codes for solving the presented theoretical tasks. Codes are implemented in the open source platform for engineering computations. The program codes given serve to illustrate the theory and demonstrate the work of the included algorithms.
410  0$aSpringerBriefs in Statistics,$x2191-544X
606   $aProbabilities
606   $aStatistics
606   $aSystem theory
606   $aComputer simulation
606   $aAlgorithms
606   $aProbability Theory and Stochastic Processes$3https://scigraph.springernature.com/ontologies/product-market-codes/M27004
606   $aStatistical Theory and Methods$3https://scigraph.springernature.com/ontologies/product-market-codes/S11001
606   $aSystems Theory, Control$3https://scigraph.springernature.com/ontologies/product-market-codes/M13070
606   $aSimulation and Modeling$3https://scigraph.springernature.com/ontologies/product-market-codes/I19000
606   $aAlgorithms$3https://scigraph.springernature.com/ontologies/product-market-codes/M14018
615  0$aProbabilities.
615  0$aStatistics.
615  0$aSystem theory.
615  0$aComputer simulation.
615  0$aAlgorithms.
615 14$aProbability Theory and Stochastic Processes.
615 24$aStatistical Theory and Methods.
615 24$aSystems Theory, Control.
615 24$aSimulation and Modeling.
615 24$aAlgorithms.
676   $a519.544
700   $aNagy$b Ivan$4aut$4http://id.loc.gov/vocabulary/relators/aut$0766766
702   $aSuzdaleva$b Evgenia$4aut$4http://id.loc.gov/vocabulary/relators/aut
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910254300703321
996   $aAlgorithms and Programs of Dynamic Mixture Estimation$92018987
997   $aUNINA