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Astrostatistics and Data Mining 2012 ed. [Kietas viršelis]

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  • Formatas: Hardback, 272 pages, aukštis x plotis: 235x155 mm, weight: 594 g, XII, 272 p., 1 Hardback
  • Serija: Springer Series in Astrostatistics 2
  • Išleidimo metai: 04-Aug-2012
  • Leidėjas: Springer-Verlag New York Inc.
  • ISBN-10: 1461433223
  • ISBN-13: 9781461433224
Kitos knygos pagal šią temą:
  • Formatas: Hardback, 272 pages, aukštis x plotis: 235x155 mm, weight: 594 g, XII, 272 p., 1 Hardback
  • Serija: Springer Series in Astrostatistics 2
  • Išleidimo metai: 04-Aug-2012
  • Leidėjas: Springer-Verlag New York Inc.
  • ISBN-10: 1461433223
  • ISBN-13: 9781461433224
Kitos knygos pagal šią temą:
????? ?This volume provides an overview of the field of Astrostatistics understood as the sub-discipline dedicated to the statistical analysis of astronomical data. It presents examples of the application of the various methodologies now available to current open issues in astronomical research. The technical aspects related to the scientific analysis of the upcoming petabyte-scale databases are emphasized given the importance that scalable Knowledge Discovery techniques will have for the full exploitation of these databases.Based on the 2011 Astrostatistics and Data Mining in Large Astronomical Databases conference and school, this volume gathers examples of the work by leading authors in the areas of Astrophysics and Statistics, including a significant contribution from the various teams that prepared for the processing and analysis of the Gaia data.

This book offers an overview of the statistical analysis of astronomical data, presenting examples that apply current methodologies to open issues in astronomical research, and exploring scientific analysis of the upcoming petabyte-scale databases.

Recenzijos

From the book reviews:

This book is the result of a 2011 Workshop on Astrostatistics and Data Mining, held on the island of in La Palma. The book provides a convenient description of many new and planned datasets, with relatively succinct statistical analyses, many of which adopt a Bayesian framework. I believe the book will be most appreciated by astronomers and applied statisticians and note that the four editors include a statistician and several astronomers. (Thomas Burr, Technometrics, Vol. 55 (4), November, 2013)

Part I Invited Talks
Recent Advances in Cosmological Bayesian Model Comparison
3(14)
Roberto Trotta
Science from Gaia: How to Deal with a Complex Billion-Source Catalogue and Data Archive
17(14)
Anthony G.A. Brown
Extracting Knowledge from Massive Astronomical Data Sets
31(16)
Massimo Brescia
Stefano Cavuoti
George S. Djorgovski
Ciro Donalek
Giuseppe Longo
Maurizio Paolillo
The Art of Data Science
47(16)
Matthew J. Graham
Part II Contributed Talks
The Distribution of Galaxies in Spectral Space
63(8)
Y. Ascasibar
J. Sanchez Almeida
Utilizing Astroinformatics to Maximize the Science Return of the Next Generation Virgo Cluster Survey
71(10)
Nicholas M. Ball
Adjustment of Observational Data to Specific Functional Forms Using a Particle Swarm Algorithm and Differential Evolution: Rotational Curves of a Spiral Galaxy as Case Study
81(8)
Miguel Cardenas-Montes
Mercedes Molla
Miguel A. Vega-Rodriguez
Juan Jose Rodriguez-Vazquez
Antonio Gomez-Iglesias
Probabilistic Description of Stellar Ensembles
89(8)
Miguel Cervino
Solar System Parameters from the Gaia Small Solar System Object Data
97(10)
Pedro David
Jerome Berthier
Daniel Hestroffer
Data Management at Gaia Data Processing Centers
107(10)
Pilar de Teodoro
Alexander Hutton
Benoit Frezouls
Alain Montmory
Jordi Portell
Rosario Messineo
Marco Riello
Krzysztof Nienartowicz
Hipparcos Variable Star Detection and Classification Efficiency
117(10)
P. Dubath
I. Lecoeur-Taibi
L. Rimoldini
M. Suveges
J. Blomme
M. Lopez
L.M. Sarro
J. De Ridder
J. Cuypers
L. Guy
K. Nienartowicz
A. Jan
M. Beck
N. Mowlavi
P. De Cat
T. Lebzelter
L. Eyer
Distributed Genetic Algorithm for Feature Selection in Gaia RVS Spectra: Application to ANN Parameterization
127(6)
Diego Fustes
Diego Ordonez
Carlos Dafonte
Minia Manteiga
Bernardino Arcay
Efficient Calculation of Covariances for Astrometric Data in the Gaia Catalogue
133(10)
Berry Holl
Lennart Lindegren
David Hobbs
Bayesian Analysis of Cosmic Structures
143(12)
Francisco-Shu Kitaura
Generalized Stellar Parametrizer with Gaia Photometry Data
155(8)
Chao Liu
Coryn A.L. Bailer-Jones
Classification of Poorly Time Sampled Light Curves of Periodic Variable Stars
163(10)
James P. Long
Joshua S. Bloom
Noureddine El Karoui
John Rice
Joseph W. Richards
Handling Imbalanced Data Sets in Multistage Classification
173(8)
M. Lopez
A New Approach to the Optimization of the Extraction of Astrometric and Photometric Information from Multi-wavelength Images in Cosmological Fields
181(10)
Maria Jose Marquez
Statistical Analysis of Caustic Crossings in Multiply Imaged Quasars
191(10)
T. Mediavilla
O. Ariza
E. Mediavilla
P. Alvarez
Stellar Age and Mass Determination
201(8)
N. Mowlavi
L. Eyer
Data Mining of the MultiDark Simulation
209(4)
Adrian M. Partl
Overcoming Sample Selection Bias in Variable Star Classification
213(10)
Joseph W. Richards
Data Mining on Ice
223(10)
Tim Ruhe
Katharina Morik
Benjamin Schowe
The Catalogue of X-Ray Bursts Detected by JEM-X Onboard INTEGRAL
233(6)
Celia Sanchez-Fernandez
The Discrete Source Classifier in Gaia-Apsis
239(8)
K.W. Smith
A Checklist for Planning Research Data Management
247(6)
Gabriel Stockle
Efficient Use of Simultaneous Multi-Band Observations for Variable Star Analysis
253(10)
Maria Suveges
Paul Bartholdi
Andrew Becker
Zeljko Ivezic
Mathias Beck
Laurent Eyer
Parametrization of Binary Stars with Gaia Observations
263
P. Tsalmantza
C.A.L. Bailer-Jones