1.

Record Nr.

UNIBAS000032796

Autore

Pirandello, Luigi

Titolo

La rallegrata / Luigi Pirandello

Pubbl/distr/stampa

Verona : <<Arnoldo>> Mondadori, 1949

Descrizione fisica

238 p. ; 18 cm

Collana

Biblioteca moderna Mondadori ; 77

Lingua di pubblicazione

Italiano

Formato

Materiale a stampa

Livello bibliografico

Monografia

2.

Record Nr.

UNINA9910254061003321

Autore

Loos Carolin

Titolo

Analysis of Single-Cell Data : ODE Constrained Mixture Modeling and Approximate Bayesian Computation / / by Carolin Loos

Pubbl/distr/stampa

Wiesbaden : , : Springer Fachmedien Wiesbaden : , : Imprint : Springer Spektrum, , 2016

ISBN

3-658-13234-5

Edizione

[1st ed. 2016.]

Descrizione fisica

1 online resource (108 p.)

Collana

BestMasters, , 2625-3615

Disciplina

510

Soggetti

Biomathematics

Mathematics - Data processing

Bioinformatics

Mathematical and Computational Biology

Computational Mathematics and Numerical Analysis

Computational and Systems Biology

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Note generali

Description based upon print version of record.

Nota di bibliografia

Includes bibliographical references.

Nota di contenuto

Modeling and Parameter Estimation for Single-Cell Data -- ODE Constrained Mixture Modeling for Multivariate Data -- Approximate



Bayesian Computation Using Multivariate Statistics.

Sommario/riassunto

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“.



3.

Record Nr.

UNINA9910300115203321

Titolo

Large-Scale and Distributed Optimization / / edited by Pontus Giselsson, Anders Rantzer

Pubbl/distr/stampa

Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018

ISBN

3-319-97478-5

Edizione

[1st ed. 2018.]

Descrizione fisica

1 online resource (XIII, 412 p. 42 illus., 33 illus. in color.)

Collana

Lecture Notes in Mathematics, , 0075-8434 ; ; 2227

Disciplina

519.3

Soggetti

Mathematical optimization

Automatic control

Electrical engineering

Optimization

Control and Systems Theory

Communications Engineering, Networks

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Sommario/riassunto

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.