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Statistical astronomy --- Congresses --- Statistical astronomy - Congresses. --- Statistique stellaire.
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"Statistics, Data Mining, and Machine Learning in Astronomy" presents a wealth of practical analysis problems, evaluates techniques for solving them, and explains how to use various approaches for different types and sizes of data sets. For all applications described in the book, Python code and example data sets are provided. The supporting data sets have been carefully selected from contemporary astronomical surveys (for example, the Sloan Digital Sky Survey) and are easy to download and use. The accompanying Python code is publicly available, well documented, and follows uniform coding standards. Together, the data sets and code enable readers to reproduce all the figures and examples, evaluate the methods, and adapt them to their own fields of interest.
Astronomy --- Statistical astronomy --- Astronomie --- Statistique stellaire. --- Data processing --- Informatique.
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Modern astronomical research is beset with a vast range of statistical challenges, ranging from reducing data from megadatasets to characterizing an amazing variety of variable celestial objects or testing astrophysical theory. Linking astronomy to the world of modern statistics, this volume is a unique resource, introducing astronomers to advanced statistics through ready-to-use code in the public domain R statistical software environment. The book presents fundamental results of probability theory and statistical inference, before exploring several fields of applied statistics, such as data smoothing, regression, multivariate analysis and classification, treatment of nondetections, time series analysis, and spatial point processes. It applies the methods discussed to contemporary astronomical research datasets using the R statistical software, making it invaluable for graduate students and researchers facing complex data analysis tasks. A link to the author's website for this book can be found at www.cambridge.org/msma. Material available on their website includes datasets, R code and errata.
Statistical astronomy --- Statistique stellaire --- Statistical astronomy. --- Science --- Astronomy. --- Statistique stellaire. --- Astronomy --- Stellar statistics --- Mathematical statistics --- Statistical methods --- R (Computer program language) --- GNU-S (Computer program language) --- Domain-specific programming languages
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"As telescopes, detectors, and computers grow ever more powerful, the volume of data at the disposal of astronomers and astrophysicists will enter the petabyte domain, providing accurate measurements for billions of celestial objects. This book provides a comprehensive and accessible introduction to the cutting-edge statistical methods needed to efficiently analyze complex data sets from astronomical surveys such as the Panoramic Survey Telescope and Rapid Response System, the Dark Energy Survey, and the upcoming Large Synoptic Survey Telescope. It serves as a practical handbook for graduate students and advanced undergraduates in physics and astronomy, and as an indispensable reference for researchers. The updates in this new edition will include fixing "code rot," correcting errata, and adding some new sections. In particular, the new sections include new material on deep learning methods, hierarchical Bayes modeling, and approximate Bayesian computation. Statistics, Data Mining, and Machine Learning in Astronomy presents a wealth of practical analysis problems, evaluates techniques for solving them, and explains how to use various approaches for different types and sizes of data sets. For all applications described in the book, Python code and example data sets are provided. The supporting data sets have been carefully selected from contemporary astronomical surveys (for example, the Sloan Digital Sky Survey) and are easy to download and use. The accompanying Python code is publicly available, well documented, and follows uniform coding standards. Together, the data sets and code enable readers to reproduce all the figures and examples, evaluate the methods, and adapt them to their own fields of interest"
Astronomy --- Statistical astronomy. --- Python (Computer program language) --- Astronomie --- Statistique stellaire. --- Python (langage de programmation) --- Data processing. --- Informatique.
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Astronomy needs statistical methods to interpret data, but statistics is a many-faceted subject that is difficult for non-specialists to access. This handbook helps astronomers analyze the complex data and models of modern astronomy. This second edition has been revised to feature many more examples using Monte Carlo simulations, and now also includes Bayesian inference, Bayes factors and Markov chain Monte Carlo integration. Chapters cover basic probability, correlation analysis, hypothesis testing, Bayesian modelling, time series analysis, luminosity functions and clustering. Exercises at the end of each chapter guide readers through the techniques and tests necessary for most observational investigations. The data tables, solutions to problems, and other resources are available online at www.cambridge.org/9780521732499. Bringing together the most relevant statistical and probabilistic techniques for use in observational astronomy, this handbook is a practical manual for advanced undergraduate and graduate students and professional astronomers.
Statistical astronomy --- Statistique stellaire --- Statistical astronomy. --- Statistics. --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics --- Astronomy --- Stellar statistics --- Mathematical statistics
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Digital sky surveys, high-precision astrometry from satellite data, deep-space data from orbiting telescopes, and the like have all increased the quantity and quality of astronomical data by orders of magnitude per year for several years. Making sense of this wealth of data requires sophisticated statistical techniques. Fortunately, statistical methodologies have similarly made great strides in recent years. Powerful synergies thus emerge when astronomers and statisticians join in examining astrostatistical problems and approaches. The book begins with an historical overview and tutorial articles on basic cosmology for statisticians and the principles of Bayesian analysis for astronomers. As in earlier volumes in this series, research contributions discussing topics in one field are joined with commentary from scholars in the other. Thus, for example, an overview of Bayesian methods for Poissonian data is joined by discussions of planning astronomical observations with optimal efficiency and nested models to deal with instrumental effects. The principal theme for the volume is the statistical methods needed to model fundamental characteristics of the early universe on its largest scales.
Statistical astronomy --- Astronomy --- Mathematical statistics --- Statistique stellaire --- EPUB-LIV-FT SPRINGER-B --- Physics. --- Observations, Astronomical. --- Space sciences. --- Cosmology. --- Statistics. --- Astronomy, Observations and Techniques. --- Extraterrestrial Physics, Space Sciences. --- Statistical Theory and Methods. --- Observations. --- Astrophysics. --- Mathematical statistics. --- Space Sciences (including Extraterrestrial Physics, Space Exploration and Astronautics). --- Mathematics --- Statistical inference --- Statistics, Mathematical --- Statistics --- Probabilities --- Sampling (Statistics) --- Astronomical physics --- Cosmic physics --- Physics --- Statistical methods --- Astronomy—Observations. --- Statistics . --- Statistical analysis --- Statistical data --- Statistical science --- Econometrics --- Deism --- Metaphysics --- Science and space --- Space research --- Cosmology --- Science --- Astronomical observations --- Observations, Astronomical
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Statistique mathématique. --- Géométrie différentielle. --- Mathematical statistics. --- Geometry, Differential. --- Mathematical statistics --- Geometry, Differential --- Mathematics --- Physical Sciences & Mathematics --- Mathematical Statistics --- Statistical inference --- Statistics, Mathematical --- Statistics --- Probabilities --- Sampling (Statistics) --- MSC 53-XX (2000) --- Systèmes différentiels involutifs --- Problème de Tammes --- Matrices de Stokes --- Équation de pullback --- Géométrie différentielle affine --- Espaces hamiltoniens --- Bernstein, Théorème de --- Anneaux déterminantiels --- Bochner, Technique de --- Calcul tensoriel --- Congruences (géométrie) --- Connexions (mathématiques) --- Coordonnées (mathématiques) --- Corps convexes --- Courbes --- Ensembles convexes --- Espaces à courbure constante --- Espaces généralisés --- Espaces symétriques --- Finsler, Espaces de --- Formes différentielles --- Formes extérieures (mathématiques) --- G-espaces --- G-structures --- Géodésiques (mathématiques) --- Géométrie différentielle globale --- Géométrie différentielle projective --- Géométrie intégrale --- Géométrie symplectique --- Groupes d'holonomie --- Hyperespace --- Lagrange, Espaces de --- Lie, Dérivées de --- Penrose, Transformation de --- Plateau, Problème de --- Quantification géométrique --- Réseaux tangentiels (géométrie différentielle) --- Riemann, Variétés de --- Géométrie de Riemann --- Sous-variétés (mathématiques) --- Structures hermitiennes --- Structures kählériennes --- Surfaces (mathématiques) --- Surfaces convexes --- Surfaces réglées --- Topologie différentielle --- Transformations (mathématiques) --- Variétés (mathématiques) --- Variétés de contact --- Variétés kählériennes --- Variétés quasi-complexes --- Variétés symplectiques --- Géométrie --- Inférence statistique --- Mathématiques --- Biométrie --- Échantillonnage --- Probabilités --- Statistique --- Sommes cumulées, Méthode des --- Analyse de régression --- Analyse de variance --- Analyse des données --- Analyse multivariée --- Analyse séquentielle --- Calcul d'erreur --- Carrés latins --- Comparaisons par paires, Méthode des --- Corrélation (statistique) --- Efficacité asymptotique (statistique) --- Estimation, Théorie de l' --- Fiabilité --- Fonctions pseudo-aléatoires --- Loi des grands nombres --- Modèles linéaires (statistique) --- Modèles non linéaires (statistique) --- Moindres carrés --- Physique statistique --- Plan d'expérience --- Prévision, Théorie de la --- Rang et sélection (statistique) --- Rupture (statistique) --- Séries chronologiques --- Statistique non paramétrique --- Statistiques robustes --- Tableaux de contingence --- Tests d'hypothèses (statistique) --- Statistique stellaire --- Differential geometry --- Statistical methods --- Méthodes statistiques --- statistique --- Differential geometry. Global analysis --- Géométrie différentielle --- Statistique mathématique
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