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Collection Springer Series in Statistics
- Editeur : Springer-Verlag
- ISSN : 0172-7397
Documents disponibles dans la collection
Affiner la recherche Interroger des sources externesMonte Carlo strategies in scientific computing (Cop. 2004) / Jun S. LIU
Titre : Monte Carlo strategies in scientific computing Type de document : texte imprimé Auteurs : Jun S. LIU, Auteur Editeur : New York : Springer-Verlag Année de publication : Cop. 2004 Collection : Springer Series in Statistics, ISSN 0172-7397 Importance : XVI-343 p. ISBN/ISSN/EAN : 978-0-387-76369-9 Langues : Anglais (eng) Mots-clés : méthode de Monte Carlo méthode statistique Résumé : A large number of scientists and engineers employ Monte Carlo simulation and related global optimization techniques (such as simulated annealing) as an essential tool in their work. For such scientists, there is a need to keep up to date with several recent advances in Monte Carlo methodologies such as cluster methods, data- augmentation, simulated tempering and other auxiliary variable methods. There is also a trend in moving towards a population-based approach. All these advances in one way or another were motivated by the need to sample from very complex distribution for which traditional methods would tend to be trapped in local energy minima. It is our aim to provide a self-contained and up to date treatment of the Monte Carlo method to this audience. The Monte Carlo method is a computer-based statistical sampling approach for solving numerical problems concerned with a complex system. The methodology was initially developed in the field of statistical physics during the early days of electronic computing (1945-55) and has now been adopted by researchers in almost all scientific fields. The fundamental idea for constructing Markov chain based Monte Carlo algorithms was introduced in the 1950s. This idea was later extended to handle more and more complex physical systems. In the 1980s, statisticians and computer scientists developed Monter Carlo-based algorithms for a wide variety of integration and optimization tasks. In the 1990s, the method began to play an important role in computational biology. Over the past fifty years, reasearchers in diverse scientific fields have studied the Monte Carlo method and contributed to its development. Today, a large number of scientisits and engineers employ Monte Carlo techniques as an essential tool in their work. For such scientists, there is a need to keep up-to-date with recent advances in Monte Carlo methodologies. Note de contenu : index, références Monte Carlo strategies in scientific computing [texte imprimé] / Jun S. LIU, Auteur . - Springer-Verlag, Cop. 2004 . - XVI-343 p.. - (Springer Series in Statistics, ISSN 0172-7397) .
ISBN : 978-0-387-76369-9
Langues : Anglais (eng)
Mots-clés : méthode de Monte Carlo méthode statistique Résumé : A large number of scientists and engineers employ Monte Carlo simulation and related global optimization techniques (such as simulated annealing) as an essential tool in their work. For such scientists, there is a need to keep up to date with several recent advances in Monte Carlo methodologies such as cluster methods, data- augmentation, simulated tempering and other auxiliary variable methods. There is also a trend in moving towards a population-based approach. All these advances in one way or another were motivated by the need to sample from very complex distribution for which traditional methods would tend to be trapped in local energy minima. It is our aim to provide a self-contained and up to date treatment of the Monte Carlo method to this audience. The Monte Carlo method is a computer-based statistical sampling approach for solving numerical problems concerned with a complex system. The methodology was initially developed in the field of statistical physics during the early days of electronic computing (1945-55) and has now been adopted by researchers in almost all scientific fields. The fundamental idea for constructing Markov chain based Monte Carlo algorithms was introduced in the 1950s. This idea was later extended to handle more and more complex physical systems. In the 1980s, statisticians and computer scientists developed Monter Carlo-based algorithms for a wide variety of integration and optimization tasks. In the 1990s, the method began to play an important role in computational biology. Over the past fifty years, reasearchers in diverse scientific fields have studied the Monte Carlo method and contributed to its development. Today, a large number of scientisits and engineers employ Monte Carlo techniques as an essential tool in their work. For such scientists, there is a need to keep up-to-date with recent advances in Monte Carlo methodologies. Note de contenu : index, références Réservation
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Code-barres Cote Support Localisation Section Disponibilité 21297 LIU/62/8994 Livre Recherche Salle Disponible The elements of statistical learning (Cop. 2009) / Trevor HASTIE
Titre : The elements of statistical learning : data mining, inference, and prediction Type de document : texte imprimé Auteurs : Trevor HASTIE, Auteur ; Robert TIBSHIRANI, Auteur ; Jerome FRIEDMAN, Auteur Mention d'édition : 2nd éd. Editeur : New York : Springer-Verlag Année de publication : Cop. 2009 Collection : Springer Series in Statistics, ISSN 0172-7397 Importance : XII-745 p. Présentation : ill. ISBN/ISSN/EAN : 978-0-387-84857-0 Langues : Anglais (eng) Mots-clés : statistique régression classification analyse des données inférence Note de contenu : index, références The elements of statistical learning : data mining, inference, and prediction [texte imprimé] / Trevor HASTIE, Auteur ; Robert TIBSHIRANI, Auteur ; Jerome FRIEDMAN, Auteur . - 2nd éd. . - Springer-Verlag, Cop. 2009 . - XII-745 p. : ill.. - (Springer Series in Statistics, ISSN 0172-7397) .
ISBN : 978-0-387-84857-0
Langues : Anglais (eng)
Mots-clés : statistique régression classification analyse des données inférence Note de contenu : index, références Réservation
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Code-barres Cote Support Localisation Section Disponibilité 22384 HAS/62/10611 a Livre Recherche Salle Disponible 22385 HAS/62/10611 b Livre Recherche Salle Disponible 21286 HAS/62/8983 Livre Recherche Salle Disponible Estimation of dependences based on empirical data (Cop. 1982) / Vladimir N. VAPNIK
Titre : Estimation of dependences based on empirical data Type de document : texte imprimé Auteurs : Vladimir N. VAPNIK, Auteur ; Samuel KOTZ, Traducteur Editeur : New York : Springer-Verlag Année de publication : Cop. 1982 Collection : Springer Series in Statistics, ISSN 0172-7397 Importance : XVI-399 p. ISBN/ISSN/EAN : 978-0-387-90733-8 Langues : Anglais (eng) Langues originales : Russe (rus) Catégories : 62C12
62G05
62H12Mots-clés : théorie de l'estimation statistique Note de contenu : index, bibliogr. Estimation of dependences based on empirical data [texte imprimé] / Vladimir N. VAPNIK, Auteur ; Samuel KOTZ, Traducteur . - Springer-Verlag, Cop. 1982 . - XVI-399 p.. - (Springer Series in Statistics, ISSN 0172-7397) .
ISBN : 978-0-387-90733-8
Langues : Anglais (eng) Langues originales : Russe (rus)
Catégories : 62C12
62G05
62H12Mots-clés : théorie de l'estimation statistique Note de contenu : index, bibliogr. Réservation
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Code-barres Cote Support Localisation Section Disponibilité 22422 VAP/62/10644 Livre Recherche Salle Disponible Nonparametric functional data analysis (Cop. 2006) / Frédéric FERRATY
Titre : Nonparametric functional data analysis : theory and practice Type de document : texte imprimé Auteurs : Frédéric FERRATY, Auteur ; Philippe VIEU, Auteur Editeur : New York : Springer-Verlag Année de publication : Cop. 2006 Collection : Springer Series in Statistics, ISSN 0172-7397 Importance : XX-258 p. ISBN/ISSN/EAN : 978-0-387-30369-7 Langues : Anglais (eng) Mots-clés : statistique non-paramétrique variable fonctionnelle statistique fonctionnelle Note de contenu : index, références Nonparametric functional data analysis : theory and practice [texte imprimé] / Frédéric FERRATY, Auteur ; Philippe VIEU, Auteur . - Springer-Verlag, Cop. 2006 . - XX-258 p.. - (Springer Series in Statistics, ISSN 0172-7397) .
ISBN : 978-0-387-30369-7
Langues : Anglais (eng)
Mots-clés : statistique non-paramétrique variable fonctionnelle statistique fonctionnelle Note de contenu : index, références Réservation
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Code-barres Cote Support Localisation Section Disponibilité 20595 FER/62/8588 Livre Recherche Salle Disponible Smoothing methods in statistics (Cop. 1996) / Jeffrey S. SIMONOFF
Titre : Smoothing methods in statistics Type de document : texte imprimé Auteurs : Jeffrey S. SIMONOFF, Auteur Editeur : New York : Springer-Verlag Année de publication : Cop. 1996 Collection : Springer Series in Statistics, ISSN 0172-7397 Importance : XII-338 p. ISBN/ISSN/EAN : 978-0-387-94716-7 Langues : Anglais (eng) Mots-clés : lissage statistique densité Note de contenu : index, références Smoothing methods in statistics [texte imprimé] / Jeffrey S. SIMONOFF, Auteur . - Springer-Verlag, Cop. 1996 . - XII-338 p.. - (Springer Series in Statistics, ISSN 0172-7397) .
ISBN : 978-0-387-94716-7
Langues : Anglais (eng)
Mots-clés : lissage statistique densité Note de contenu : index, références Réservation
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Code-barres Cote Support Localisation Section Disponibilité 20571 SIM/62/8567 Livre Recherche Salle Disponible Nonparametric smoothing and lack-of-fit tests (Cop. 1997) / Jeffrey D. HART
PermalinkInterpolation of spatial data (Cop. 1999) / Michael L. STEIN
PermalinkA distribution-free theory of nonparametric regression (Cop. 2002) / László GYÖRFI
PermalinkA course on point processes (Cop. 1993) / Rolf-Dieter REISS
PermalinkSmoothing spline ANOVA models (Cop. 2002) / Chong GU
PermalinkThe Jackknife and bootstrap (Cop. 1995) / Jun SHAO
PermalinkRobust asymptotic statistics (1994) / Helmut RIEDER
PermalinkAsymptotic methods in statistical decision theory (Cop. 1986) / Lucien LE CAM
PermalinkThe bootstrap and edgeworth expansion (Cop. 1992) / Peter HALL
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