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1. |
Record Nr. |
UNIBAS000032796 |
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Autore |
Pirandello, Luigi |
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Titolo |
La rallegrata / Luigi Pirandello |
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Pubbl/distr/stampa |
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Verona : <<Arnoldo>> Mondadori, 1949 |
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Descrizione fisica |
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Collana |
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Biblioteca moderna Mondadori ; 77 |
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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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2. |
Record Nr. |
UNINA9910254061003321 |
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Autore |
Loos Carolin |
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Titolo |
Analysis of Single-Cell Data : ODE Constrained Mixture Modeling and Approximate Bayesian Computation / / by Carolin Loos |
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Pubbl/distr/stampa |
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Wiesbaden : , : Springer Fachmedien Wiesbaden : , : Imprint : Springer Spektrum, , 2016 |
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ISBN |
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Edizione |
[1st ed. 2016.] |
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Descrizione fisica |
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1 online resource (108 p.) |
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Collana |
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Disciplina |
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Soggetti |
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Biomathematics |
Mathematics - Data processing |
Bioinformatics |
Mathematical and Computational Biology |
Computational Mathematics and Numerical Analysis |
Computational and Systems Biology |
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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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Description based upon print version of record. |
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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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Modeling and Parameter Estimation for Single-Cell Data -- ODE Constrained Mixture Modeling for Multivariate Data -- Approximate |
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Bayesian Computation Using Multivariate Statistics. |
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Sommario/riassunto |
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Carolin Loos introduces two novel approaches for the analysis of single-cell data. Both approaches can be used to study cellular heterogeneity and therefore advance a holistic understanding of biological processes. The first method, ODE constrained mixture modeling, enables the identification of subpopulation structures and sources of variability in single-cell snapshot data. The second method estimates parameters of single-cell time-lapse data using approximate Bayesian computation and is able to exploit the temporal cross-correlation of the data as well as lineage information. Contents Modeling and Parameter Estimation for Single-Cell Data ODE Constrained Mixture Modeling for Multivariate Data Approximate Bayesian Computation Using Multivariate Statistics Target Groups Researchers and students in the fields of (bio-)mathematics, statistics, bioinformatics System biologists, biostatisticians, bioinformaticians The Author Carolin Loos is currently doing her PhD at the Institute of Computational Biology at the Helmholtz Zentrum München. She is member of the junior research group „Data-driven Computational Modeling“. |
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3. |
Record Nr. |
UNINA9910300115203321 |
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Titolo |
Large-Scale and Distributed Optimization / / edited by Pontus Giselsson, Anders Rantzer |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 |
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ISBN |
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Edizione |
[1st ed. 2018.] |
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Descrizione fisica |
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1 online resource (XIII, 412 p. 42 illus., 33 illus. in color.) |
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Collana |
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Lecture Notes in Mathematics, , 0075-8434 ; ; 2227 |
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Disciplina |
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Soggetti |
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Mathematical optimization |
Automatic control |
Electrical engineering |
Optimization |
Control and Systems Theory |
Communications Engineering, Networks |
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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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Sommario/riassunto |
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This book presents tools and methods for large-scale and distributed optimization. Since many methods in "Big Data" fields rely on solving large-scale optimization problems, often in distributed fashion, this topic has over the last decade emerged to become very important. As well as specific coverage of this active research field, the book serves as a powerful source of information for practitioners as well as theoreticians. Large-Scale and Distributed Optimization is a unique combination of contributions from leading experts in the field, who were speakers at the LCCC Focus Period on Large-Scale and Distributed Optimization, held in Lund, 14th–16th June 2017. A source of information and innovative ideas for current and future research, this book will appeal to researchers, academics, and students who are interested in large-scale optimization. |
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