Deformation theory / Robin HARTSHORNE (cop. 2010)

Public ISBD 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 : 978-1-4419-1595-5 Langues : Anglais Catégories : 14B07 14B10 14B12 Mots-clés : déformation  théorie de la déformation Note de contenu : index, références Deformation theory [texte imprimé] / Robin HARTSHORNE, Auteur . - New York : Springer-Verlag, cop. 2010 . - VI-234 p.. - (Graduate Texts in Mathematics, ISSN 0072-5285; 257) . ISBN : 978-1-4419-1595-5 Langues : Anglais Catégories : 14B07 14B10 14B12 Mots-clés : déformation  théorie de la déformation Note de contenu : index, références

Exemplaires

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 / John STILLWELL (Cop. 2008)

Public ISBD 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 Mots-clés : théorie de Lie  algèbre de Lie  topologie  groupe Note de contenu : index, bibliogr. Naive Lie theory [texte imprimé] / John STILLWELL, Auteur . - New York : Springer-Verlag, Cop. 2008 . - XIII-217 p.. - (Undergraduate Texts in Mathematics) . ISBN : 978-0-387-78214-0 Langues : Anglais Mots-clés : théorie de Lie  algèbre de Lie  topologie  groupe Note de contenu : index, bibliogr.

Exemplaires

Code-barres	Cote	Support	Localisation	Section	Disponibilité
21323	STI/22/9018	Livre	Recherche	Salle	Disponible

Wave propagation and time reversal in randomly layered media / Jean-Pierre FOUQUE (2007)

Public ISBD 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 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 . - New York : Springer-Verlag, 2007 . - XX-612 p.. - (Stochastic Modelling and Applied Probability, ISSN 0172-4568; 56) . ISBN : 978-0-387-30890-6 Langues : Anglais Mots-clés : processus stochastique  variable aléatoire  propagation des vagues Note de contenu : index, références

Exemplaires

Code-barres	Cote	Support	Localisation	Section	Disponibilité
21316	FOU/35/9013	Livre	Recherche	Salle	Disponible

Monte Carlo method in financial engineering / Paul GLASSERMAN (1996)

Public ISBD 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 Mots-clés : méthode de Monte Carlo  ingénierie financière Note de contenu : index, références Monte Carlo method in financial engineering [texte imprimé] / Paul GLASSERMAN, Auteur . - New York : Springer-Verlag, 1996 . - XIII-596 p.. - (Applications of Mathematics, ISSN 0172-4568; 53) . ISBN : 978-0-387-00451-8 Langues : Anglais Mots-clés : méthode de Monte Carlo  ingénierie financière Note de contenu : index, références

Exemplaires

Code-barres	Cote	Support	Localisation	Section	Disponibilité
21296	GLA/62/8953	Livre	Recherche	Salle	Disponible

Monte Carlo strategies in scientific computing / Jun S. LIU (Cop. 2004)

Public ISBD 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 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 . - New York : Springer-Verlag, Cop. 2004 . - XVI-343 p.. - (Springer Series in Statistics, ISSN 0172-7397) . ISBN : 978-0-387-76369-9 Langues : Anglais 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

Exemplaires

Code-barres	Cote	Support	Localisation	Section	Disponibilité
21297	LIU/62/8994	Livre	Recherche	Salle	Disponible

Monte Carlo / George S. FISHMAN (cop. 1996)

Permalink

Graph theory / J.A. BONDY (Cop. 2008)

Permalink

The cross-entropy method / Reuven Y. RUBINSTEIN (Cop. 2004)

Permalink

The elements of statistical learning / Trevor HASTIE (Cop. 2009)

Permalink

Convex and discrete geometry / Peter M. GRUBER (Cop. 2007)

Permalink

Computational statistics / James E. GENTLE (Cop. 2009)

Permalink

Bayesian core: a practical approach to computational bayesian statistics / Jean-Michel MARIN (Cop. 2007)

Permalink

Theory of multivariate stastistics / Martin BILODEAU (Cop. 1999)

Permalink

Bayesian computation with R / Jim ALBERT (2009) 

Permalink

Implicit functions and solutions mappings / Asen L. DONTCHEV (2009)

Permalink

---

page /26