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1. |
Record Nr. |
UNISALENTO991003520799707536 |
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Autore |
Sambrook, Joseph |
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Titolo |
Molecular cloning : a laboratory manual / J. Sambrook, E. F. Fritsch, T. Maniatis |
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Pubbl/distr/stampa |
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Cold Spring Harbor : Cold Spring Harbor Laboratory Press, 1989 |
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Edizione |
[2nd ed.] |
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Descrizione fisica |
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Altri autori (Persone) |
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Maniatis, Tom |
Fritsch, E. F. |
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Disciplina |
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Soggetti |
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Eukaryotic cells - Laboratory manuals |
Molecular cloning - Laboratory manuals |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Includes bibliographical references and indexes |
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2. |
Record Nr. |
UNINA9910254300703321 |
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Autore |
Nagy Iván |
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Titolo |
Algorithms and Programs of Dynamic Mixture Estimation : Unified Approach to Different Types of Components / / by Ivan Nagy, Evgenia Suzdaleva |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 |
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ISBN |
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Edizione |
[1st ed. 2017.] |
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Descrizione fisica |
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1 online resource (113 pages) : illustrations, tables |
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Collana |
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SpringerBriefs in Statistics, , 2191-5458 |
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Disciplina |
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Soggetti |
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Probabilities |
Statistics |
System theory |
Control theory |
Computer simulation |
Algorithms |
Probability Theory |
Statistical Theory and Methods |
Systems Theory, Control |
Computer Modelling |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references. |
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Nota di contenuto |
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Introduction -- Basic Models -- Statistical Analysis of Dynamic Mixtures -- Dynamic Mixture Estimation -- Program Codes -- Experiments -- Appendices. |
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Sommario/riassunto |
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This 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 |
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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. |
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