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Statistical Analysis of Network Data with R / Gábor Csárdi (u. a.) / Taschenbuch / Use R! / Paperback / XIV / Englisch / 2020 / Springer International Publishing / EAN 9783030441289 - Taschenbuch

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Statistical Analysis of Network Data with R | Gábor Csárdi (u. a.) | Taschenbuch | Use R! | Paperback | XIV | Englisch | 2020 | Springer International Publishing | EAN 9783030441289 - Taschenbuch

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Statistical Analysis of Network Data with R (Use R!) - Taschenbuch

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Statistical Analysis of Network Data with R (Use R!)
Autor:
Titel:
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3030441288

Detailangaben zum Buch - Statistical Analysis of Network Data with R (Use R!)


EAN (ISBN-13): 9783030441289
ISBN (ISBN-10): 3030441288
Taschenbuch
Erscheinungsjahr: 2020
Herausgeber: Springer

Buch in der Datenbank seit 2020-11-01T12:25:10+01:00 (Berlin)
Detailseite zuletzt geändert am 2023-11-21T17:09:21+01:00 (Berlin)
ISBN/EAN: 3030441288

ISBN - alternative Schreibweisen:
3-030-44128-8, 978-3-030-44128-9
Alternative Schreibweisen und verwandte Suchbegriffe:
Titel des Buches: statistical analysis, gabor

Daten vom Verlag:

Autor/in: Eric D. Kolaczyk; Gábor Csárdi
Titel: Use R! Statistical Analysis of Network Data with R
Verlag: Springer; Springer International Publishing
228 Seiten
Erscheinungsjahr: 2020-06-03
Cham; CH
Gedruckt / Hergestellt in Niederlande.
Sprache: Englisch
80,24 € (DE)
82,49 € (AT)
88,50 CHF (CH)
POD
XIV, 228 p. 75 illus., 56 illus. in color.

BC; Hardcover, Softcover / Mathematik/Wahrscheinlichkeitstheorie, Stochastik, Mathematische Statistik; Wahrscheinlichkeitsrechnung und Statistik; Verstehen; Statistical Analysis; Network Data; Dynamic Networks; Static Networks; Network Analysis; igraph; Network Graph; Network Flow Data; Graph Visualization; Networked Experiments; R Software; R Package; Statistics and Computing; Computer Communication Networks; Communications Engineering, Networks; Mathematische und statistische Software; Netzwerk-Hardware; Nachrichtententechnik, Telekommunikation; BC; EA

The new edition of this book provides an easily accessible introduction to the statistical analysis of network data using R. It has been fully revised and can be used as a stand-alone resource in which multiple R packages are used to illustrate how to conduct a wide range of network analyses, from basic manipulation and visualization, to summary and characterization, to modeling of network data. The central package is igraph, which provides extensive capabilities for studying network graphs in R. The new edition of this book includes an overhaul to recent changes in igraph. The material in this book is organized to flow from descriptive statistical methods to topics centered on modeling and inference with networks, with the latter separated into two sub-areas, corresponding first to the modeling and inference of networks themselves, and then, to processes on networks. 

Statistical Analysis of Network Data with R, 2nd Ed.

is a professor of statistics and a data science faculty fellow at Boston University, in the Department of Mathematics and Statistics, where he also is an affiliated faculty member in the Bioinformatics Program, the Division of Systems Engineering, and the Center for Systems Neuroscience.  Currently, he serves as the director of Boston University's Hariri Institute for Computing.  His publications on network-based topics, beyond the development of statistical methodology and theory, include work on applications ranging from the detection of anomalous traffic patterns in computer networks to the prediction of biological function in networks of interacting proteins to the characterization of influence of groups of actors in social networks. He is an elected fellow of the American Association for the Advancement of Science (AAAS), the American Statistical Association (ASA), and the Institute of Mathematical Statistics, an elected member of the International Statistical Institute (ISI), and an elected senior member of the Institute of Electrical and Electronics Engineers (IEEE).

Gábor Csárdi

Eric D. Kolaczyk

The new edition of this book provides an easily accessible introduction to the statistical analysis of network data using R. It has been fully revised and can be used as a stand-alone resource in which multiple R packages are used to illustrate how to conduct a wide range of network analyses, from basic manipulation and visualization, to summary and characterization, to modeling of network data. The central package is igraph, which provides extensive capabilities for studying network graphs in R. The new edition of this book includes an overhaul to recent changes in igraph. The material in this book is organized to flow from descriptive statistical methods to topics centered on modeling and inference with networks, with the latter separated into two sub-areas, corresponding first to the modeling and inference of networks themselves, and then, to processes on networks. 

Statistical Analysis of Network Data with R, 2nd Ed.
Presents a fully updated and easily accessible introduction to statistical methods of network analysis and their implementation in R New edition includes a chapter covering networked experiments and an overhaul to the R code for igraph Accessible for researchers in other quantitative fields or practitioners in applied areas that need to analyze network data Includes supplementary material: sn.pub/extras
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