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Refinar búsquedaAstrostatistical Challenges for the New Astronomy / SpringerLink (Online service) ; Hilbe, Joseph M (2013)
Título : Astrostatistical Challenges for the New Astronomy Tipo de documento: documento electrónico Autores: SpringerLink (Online service) ; Hilbe, Joseph M Editorial: New York, NY : Springer New York Fecha de publicación: 2013 Otro editor: Imprint: Springer Colección: Springer Series in Astrostatistics, ISSN 2199-1030 num. 1 Número de páginas: XIV, 238 p Il.: online resource ISBN/ISSN/DL: 978-1-4614-3508-2 Idioma : Inglés (eng) Palabras clave: Statistics Astronomy Astrophysics Cosmology Statistical Theory and Methods Computing/Statistics Programs Astronomy, Clasificación: 51 Matemáticas Resumen: Astrostatistical Challenges for the New Astronomy presents a collection of monographs authored by several of the disciplines leading astrostatisticians, i.e. by researchers from the fields of statistics and astronomy-astrophysics having in interest in the statistical analysis of astronomical and cosmological data. Eight of the ten monographs are enhancements of presentations given by the authors as invited or special topics in astrostatistics papers at the ISI World Statistics Congress (2011, Dublin, Ireland). The opening chapter, by the editor, was adapted from an invited seminar given at Los Alamos National Laboratory (2011) on the history and current state of the discipline; the second chapter by Thomas Loredo was adapted from his invited presentation at the Statistical Challenges in Modern Astronomy V conference (2011, Pennsylvania State University), presenting insights regarding frequentist and Bayesian methods of estimation in astrostatistical analysis. The remaining monographs are research papers discussing various topics in astrostatistics. The monographs provide the reader with an excellent overview of the current state astrostatistical research, and offer guidelines as to subjects of future research Nota de contenido: Joseph Hilbe, Jet Propulsion Laboratory and Arizona State University, Astrostatistics: A brief history and view to the future -- Thomas Loredo, Cornell Univ, Bayesian astrostatistics: A backward look to the future -- Stefano Andreon, INAF-Osservatorio Astronomico di Brera, Italy, Understanding better (some) astronomical data using Bayesian methods -- Martin Kunz, Institute for Theoretical Physics, Univ of Geneva, BEAMS: separating the wheat from the chaff in supernova analysis -- Benjamin Wandelt, Institut d'Astrophysique de Paris, Université Pierre et Marie Curie, France, Cosmostatistics -- Roberto Trotta, Astrophysics Group, Dept of Physics, Imperial College London (with Farhan Feroz (Cambridge), Mike Hobson (Cambridge), and Roberto Ruiz de Austri (Univ of Valencia, Spain), Recent advances in Bayesian inference in cosmology and astroparticle physics thanks to the Multinest Algorithm -- Phillip Gregory, Department of Astronomy, Univ of British Columbia, Canada, Extrasolar planets via Bayesian model fitting -- Marc Henrion, Dept of Mathematics, Imperial College, London, UK (with Daniel Mortlock (Imperial), Axel Gandy (Imperial), and David J. Hand (Imperial)), Subspace methods for anomaly detection in high dimensional astronomical databases -- Asis Kumar Chattopadhyay, Dept of Statistics, Univ of Calcutta, India (with Tanuka Chattyopadhyay, Tuli De, and Saptarshi Mondal), Independent Component Analysis for dimension reduction classification: Hough transform and CASH Algorithm -- Marisa March, Astrophysics Group, Dept of Physics, Imperial College London (with Roberto Trotta), Improved cosmological constraints from a Bayesian hierarchical model of supernova type Ia data En línea: http://dx.doi.org/10.1007/978-1-4614-3508-2 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=32207 Astrostatistical Challenges for the New Astronomy [documento electrónico] / SpringerLink (Online service) ; Hilbe, Joseph M . - New York, NY : Springer New York : Imprint: Springer, 2013 . - XIV, 238 p : online resource. - (Springer Series in Astrostatistics, ISSN 2199-1030; 1) .
ISBN : 978-1-4614-3508-2
Idioma : Inglés (eng)
Palabras clave: Statistics Astronomy Astrophysics Cosmology Statistical Theory and Methods Computing/Statistics Programs Astronomy, Clasificación: 51 Matemáticas Resumen: Astrostatistical Challenges for the New Astronomy presents a collection of monographs authored by several of the disciplines leading astrostatisticians, i.e. by researchers from the fields of statistics and astronomy-astrophysics having in interest in the statistical analysis of astronomical and cosmological data. Eight of the ten monographs are enhancements of presentations given by the authors as invited or special topics in astrostatistics papers at the ISI World Statistics Congress (2011, Dublin, Ireland). The opening chapter, by the editor, was adapted from an invited seminar given at Los Alamos National Laboratory (2011) on the history and current state of the discipline; the second chapter by Thomas Loredo was adapted from his invited presentation at the Statistical Challenges in Modern Astronomy V conference (2011, Pennsylvania State University), presenting insights regarding frequentist and Bayesian methods of estimation in astrostatistical analysis. The remaining monographs are research papers discussing various topics in astrostatistics. The monographs provide the reader with an excellent overview of the current state astrostatistical research, and offer guidelines as to subjects of future research Nota de contenido: Joseph Hilbe, Jet Propulsion Laboratory and Arizona State University, Astrostatistics: A brief history and view to the future -- Thomas Loredo, Cornell Univ, Bayesian astrostatistics: A backward look to the future -- Stefano Andreon, INAF-Osservatorio Astronomico di Brera, Italy, Understanding better (some) astronomical data using Bayesian methods -- Martin Kunz, Institute for Theoretical Physics, Univ of Geneva, BEAMS: separating the wheat from the chaff in supernova analysis -- Benjamin Wandelt, Institut d'Astrophysique de Paris, Université Pierre et Marie Curie, France, Cosmostatistics -- Roberto Trotta, Astrophysics Group, Dept of Physics, Imperial College London (with Farhan Feroz (Cambridge), Mike Hobson (Cambridge), and Roberto Ruiz de Austri (Univ of Valencia, Spain), Recent advances in Bayesian inference in cosmology and astroparticle physics thanks to the Multinest Algorithm -- Phillip Gregory, Department of Astronomy, Univ of British Columbia, Canada, Extrasolar planets via Bayesian model fitting -- Marc Henrion, Dept of Mathematics, Imperial College, London, UK (with Daniel Mortlock (Imperial), Axel Gandy (Imperial), and David J. Hand (Imperial)), Subspace methods for anomaly detection in high dimensional astronomical databases -- Asis Kumar Chattopadhyay, Dept of Statistics, Univ of Calcutta, India (with Tanuka Chattyopadhyay, Tuli De, and Saptarshi Mondal), Independent Component Analysis for dimension reduction classification: Hough transform and CASH Algorithm -- Marisa March, Astrophysics Group, Dept of Physics, Imperial College London (with Roberto Trotta), Improved cosmological constraints from a Bayesian hierarchical model of supernova type Ia data En línea: http://dx.doi.org/10.1007/978-1-4614-3508-2 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=32207 Ejemplares
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Título : Astrostatistics and Data Mining Tipo de documento: documento electrónico Autores: SpringerLink (Online service) ; Sarro, Luis Manuel ; Eyer, Laurent ; O'Mullane, William ; De Ridder, Joris Editorial: New York, NY : Springer New York Fecha de publicación: 2012 Otro editor: Imprint: Springer Colección: Springer Series in Astrostatistics, ISSN 2199-1030 num. 2 Número de páginas: XII, 272 p Il.: online resource ISBN/ISSN/DL: 978-1-4614-3323-1 Idioma : Inglés (eng) Palabras clave: Physics Astronomy Astrophysics Cosmology Statistics Astronomy, and Statistics, general Astroparticles Statistical Theory Methods Clasificación: 51 Matemáticas Resumen: 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 Nota de contenido: ??? 'Science with Gaia: how will we deal with a complex billion-source catalogue and data archive?' by Anthony Brown (Leiden University,Netherlads) -- 'Recent Advances in cosmological Bayesian model comparison' by Roberto Trotta (University College London, UK) -- 'The Art of Data Science' by Matthew Graham (Center for Advanced Computing Research, California Institute of Technology, USA) -- 'Astronomical Surveys: from SDSS to LSST' by Robert Lupton (Princeton University, USA) -- 'Exoplanet demography, quasar target selection, and probabilistic redshift estimation: Hierarchical models for density estimation, classification, and regression.' by David Hogg (New York University, USA) -- 'Learning to disentangle Exoplanet signals from correlated noise' by Suzanne Aigrain (Oxford University, UK) -- Astroinformatics and data mining: how to cope with the data tsunami' by Giuseppe Longo (Federico II University, Italy) -- Advanced statistical techniques for the processing of astronomical data: time series, images, low number statistics for high energy photons, heteroskedastic data, non-detections -- Challenges in the data mining of astronomical databases: the class imbalance in training sets or how to define prior robust preprocessing for supervised/unsupervised classification robust inference with heterogeneous datasets, how to combine observations, models, priors, etc in a training/test set error propagation -- The challenge of petabyte size databases: scalability, parallel computing, accuracy -- Geometric data organization, sky indexing for efficient data retrieval, intelligent access to petabyte size databases -- Knowledge Discovery in astronomical archives: outlier detection, new object types, parametric inference, model fitting and model selection, etc -- Combining the classical domain knowledge approach with machine learning techniques -- Global approaches for global datasets. The Galaxy zoo and the Universe zoo -- The Virtual Observatories, Data Mining and Astrostatistics: software, standards, protocols. En línea: http://dx.doi.org/10.1007/978-1-4614-3323-1 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=32797 Astrostatistics and Data Mining [documento electrónico] / SpringerLink (Online service) ; Sarro, Luis Manuel ; Eyer, Laurent ; O'Mullane, William ; De Ridder, Joris . - New York, NY : Springer New York : Imprint: Springer, 2012 . - XII, 272 p : online resource. - (Springer Series in Astrostatistics, ISSN 2199-1030; 2) .
ISBN : 978-1-4614-3323-1
Idioma : Inglés (eng)
Palabras clave: Physics Astronomy Astrophysics Cosmology Statistics Astronomy, and Statistics, general Astroparticles Statistical Theory Methods Clasificación: 51 Matemáticas Resumen: 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 Nota de contenido: ??? 'Science with Gaia: how will we deal with a complex billion-source catalogue and data archive?' by Anthony Brown (Leiden University,Netherlads) -- 'Recent Advances in cosmological Bayesian model comparison' by Roberto Trotta (University College London, UK) -- 'The Art of Data Science' by Matthew Graham (Center for Advanced Computing Research, California Institute of Technology, USA) -- 'Astronomical Surveys: from SDSS to LSST' by Robert Lupton (Princeton University, USA) -- 'Exoplanet demography, quasar target selection, and probabilistic redshift estimation: Hierarchical models for density estimation, classification, and regression.' by David Hogg (New York University, USA) -- 'Learning to disentangle Exoplanet signals from correlated noise' by Suzanne Aigrain (Oxford University, UK) -- Astroinformatics and data mining: how to cope with the data tsunami' by Giuseppe Longo (Federico II University, Italy) -- Advanced statistical techniques for the processing of astronomical data: time series, images, low number statistics for high energy photons, heteroskedastic data, non-detections -- Challenges in the data mining of astronomical databases: the class imbalance in training sets or how to define prior robust preprocessing for supervised/unsupervised classification robust inference with heterogeneous datasets, how to combine observations, models, priors, etc in a training/test set error propagation -- The challenge of petabyte size databases: scalability, parallel computing, accuracy -- Geometric data organization, sky indexing for efficient data retrieval, intelligent access to petabyte size databases -- Knowledge Discovery in astronomical archives: outlier detection, new object types, parametric inference, model fitting and model selection, etc -- Combining the classical domain knowledge approach with machine learning techniques -- Global approaches for global datasets. The Galaxy zoo and the Universe zoo -- The Virtual Observatories, Data Mining and Astrostatistics: software, standards, protocols. En línea: http://dx.doi.org/10.1007/978-1-4614-3323-1 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=32797 Ejemplares
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