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Documents disponibles chez cet éditeur
Affiner la recherche Interroger des sources externesDeformation theory (cop. 2010) / Robin HARTSHORNE
Titre : Deformation theory Type de document : texte imprimé Auteurs : Robin HARTSHORNE, Auteur Editeur : New York : Springer-Verlag Année de publication : cop. 2010 Collection : Graduate Texts in Mathematics, ISSN 0072-5285 num. 257 Importance : VI-234 p. ISBN/ISSN/EAN : 978-1-441-91595-5 Langues : Anglais (eng) Catégories : 14B07
14B10
14B12Mots-clés : déformation théorie de la déformation Note de contenu : index, références Deformation theory [texte imprimé] / Robin HARTSHORNE, Auteur . - Springer-Verlag, cop. 2010 . - VI-234 p.. - (Graduate Texts in Mathematics, ISSN 0072-5285; 257) .
ISBN : 978-1-441-91595-5
Langues : Anglais (eng)
Catégories : 14B07
14B10
14B12Mots-clés : déformation théorie de la déformation Note de contenu : index, références Réservation
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Code-barres Cote Support Localisation Section Disponibilité 21321 HAR/14/9016 Livre Recherche Salle Disponible 21428 HAR/14/9095 Livre Recherche Salle Disponible Naive Lie theory (Cop. 2008) / John STILLWELL
Titre : Naive Lie theory Type de document : texte imprimé Auteurs : John STILLWELL, Auteur Editeur : New York : Springer-Verlag Année de publication : Cop. 2008 Collection : Undergraduate Texts in Mathematics Importance : XIII-217 p. ISBN/ISSN/EAN : 978-0-387-78214-0 Langues : Anglais (eng) Mots-clés : théorie de Lie algèbre de Lie topologie groupe Note de contenu : index, bibliogr. Naive Lie theory [texte imprimé] / John STILLWELL, Auteur . - Springer-Verlag, Cop. 2008 . - XIII-217 p.. - (Undergraduate Texts in Mathematics) .
ISBN : 978-0-387-78214-0
Langues : Anglais (eng)
Mots-clés : théorie de Lie algèbre de Lie topologie groupe Note de contenu : index, bibliogr. Réservation
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Code-barres Cote Support Localisation Section Disponibilité 21323 STI/22/9018 Livre Recherche Salle Disponible Wave propagation and time reversal in randomly layered media (2007) / Jean-Pierre FOUQUE
Titre : Wave propagation and time reversal in randomly layered media Type de document : texte imprimé Auteurs : Jean-Pierre FOUQUE, Auteur ; Josselin GARNIER, Auteur ; George PAPANICOLAOU, Auteur Editeur : New York : Springer-Verlag Année de publication : 2007 Collection : Stochastic Modelling and Applied Probability, ISSN 0172-4568 num. 56 Importance : XX-612 p. ISBN/ISSN/EAN : 978-0-387-30890-6 Langues : Anglais (eng) Mots-clés : processus stochastique variable aléatoire propagation des vagues Note de contenu : index, références Wave propagation and time reversal in randomly layered media [texte imprimé] / Jean-Pierre FOUQUE, Auteur ; Josselin GARNIER, Auteur ; George PAPANICOLAOU, Auteur . - Springer-Verlag, 2007 . - XX-612 p.. - (Stochastic Modelling and Applied Probability, ISSN 0172-4568; 56) .
ISBN : 978-0-387-30890-6
Langues : Anglais (eng)
Mots-clés : processus stochastique variable aléatoire propagation des vagues Note de contenu : index, références Réservation
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Code-barres Cote Support Localisation Section Disponibilité 21316 FOU/35/9013 Livre Recherche Salle Disponible Monte Carlo method in financial engineering (1996) / Paul GLASSERMAN
Titre : Monte Carlo method in financial engineering Type de document : texte imprimé Auteurs : Paul GLASSERMAN, Auteur Editeur : New York : Springer-Verlag Année de publication : 1996 Collection : Applications of Mathematics, ISSN 0172-4568 num. 53 Importance : XIII-596 p. ISBN/ISSN/EAN : 978-0-387-00451-8 Langues : Anglais (eng) Mots-clés : méthode de Monte Carlo ingénierie financière Note de contenu : index, références Monte Carlo method in financial engineering [texte imprimé] / Paul GLASSERMAN, Auteur . - Springer-Verlag, 1996 . - XIII-596 p.. - (Applications of Mathematics, ISSN 0172-4568; 53) .
ISBN : 978-0-387-00451-8
Langues : Anglais (eng)
Mots-clés : méthode de Monte Carlo ingénierie financière Note de contenu : index, références Réservation
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Code-barres Cote Support Localisation Section Disponibilité 21296 GLA/62/8953 Livre Recherche Salle Disponible Monte 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 Monte Carlo (cop. 1996) / George S. FISHMAN
PermalinkGraph theory (Cop. 2008) / J.A. BONDY
PermalinkThe cross-entropy method (Cop. 2004) / Reuven Y. RUBINSTEIN
PermalinkThe elements of statistical learning (Cop. 2009) / Trevor HASTIE
PermalinkConvex and discrete geometry (Cop. 2007) / Peter M. GRUBER
PermalinkComputational statistics (Cop. 2009) / James E. GENTLE
PermalinkBayesian core: a practical approach to computational bayesian statistics (Cop. 2007) / Jean-Michel MARIN
PermalinkTheory of multivariate stastistics (Cop. 1999) / Martin BILODEAU
PermalinkPermalinkImplicit functions and solutions mappings (2009) / Asen L. DONTCHEV
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