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'Monte Carlo method'




Handbook of Markov chain Monte Carlo / Brooks, S.(Ed.) ; Gelman, A.(Ed.) ; Jones, G.L.(Ed.) ; Meng, X.-L.(Ed.) (2011)
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Titre : Handbook of Markov chain Monte Carlo Type de document : livre Auteurs : Brooks, S.(Ed.) ; Gelman, A.(Ed.) ; Jones, G.L.(Ed.) ; Meng, X.-L.(Ed.) Editeur : Boca Raton, Florida : CRC Press Année de publication : 2011 Collection : Chapman & Hall/CRC Handbooks of modern statistical methods Importance : 592 p. ISBN/ISSN/EAN : 978-1-4200-7941-8 Langues : Anglais (eng) Mots-clés : Case studies Markov processes Monte Carlo method Statistical methods En ligne : http://www.crcpress.com/product/isbn/9781420079418 Handbook of Markov chain Monte Carlo [livre] / Brooks, S.(Ed.) ; Gelman, A.(Ed.) ; Jones, G.L.(Ed.) ; Meng, X.-L.(Ed.) . - Boca Raton, Florida : CRC Press, 2011 . - 592 p.. - (Chapman & Hall/CRC Handbooks of modern statistical methods) .
ISBN : 978-1-4200-7941-8
Langues : Anglais (eng)
Mots-clés : Case studies Markov processes Monte Carlo method Statistical methods En ligne : http://www.crcpress.com/product/isbn/9781420079418 Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 68963 BRO_11_68963 Livre Salle des ouvrages 11_Mathématiques Sorti jusqu'au 25/05/2043
Titre : Introducing Monte Carlo methods with R Type de document : livre Auteurs : Christian P. Robert, Auteur ; George Casella, Auteur Editeur : New York : Springer Année de publication : 2010 Collection : Use R! Importance : 283 p. ISBN/ISSN/EAN : 978-1-4419-1575-7 Note générale : DOI: 10.1007/978-1-4419-1576-4 Langues : Anglais (eng) Mots-clés : Computer programs Data processing Markov processes Mathematical statistics Monte Carlo method R (Computer program language) En ligne : http://dx.doi.org/10.1007/978-1-4419-1576-4 Introducing Monte Carlo methods with R [livre] / Christian P. Robert, Auteur ; George Casella, Auteur . - New York : Springer, 2010 . - 283 p.. - (Use R!) .
ISBN : 978-1-4419-1575-7
DOI: 10.1007/978-1-4419-1576-4
Langues : Anglais (eng)
Mots-clés : Computer programs Data processing Markov processes Mathematical statistics Monte Carlo method R (Computer program language) En ligne : http://dx.doi.org/10.1007/978-1-4419-1576-4 Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 68758 ROB_11_68758 Livre Salle des ouvrages 11_Mathématiques Disponible
Titre : Sequential Monte Carlo methods in practice Type de document : livre Auteurs : Doucet, A.(Ed.), Éditeur scientifique ; de Freitas, N.(Ed.), Éditeur scientifique ; Gordon, N.(Ed.), Éditeur scientifique Editeur : New York : Springer Année de publication : 2001 Collection : Statistics for engineering and information science Importance : 581 p. ISBN/ISSN/EAN : 978-0-387-95146-1 Langues : Anglais (eng) Mots-clés : Monte Carlo method Statistical methods Résumé : Le site éditeur indique : Monte Carlo methods are revolutionising the on-line analysis of data in fields as diverse as financial modelling, target tracking and computer vision. These methods, appearing under the names of bootstrap filters, condensation, optimal Monte Carlo filters, particle filters and survial of the fittest, have made it possible to solve numerically many complex, non-standarard problems that were previously intractable. This book presents the first comprehensive treatment of these techniques, including convergence results and applications to tracking, guidance, automated target recognition, aircraft navigation, robot navigation, econometrics, financial modelling, neural networks,optimal control, optimal filtering, communications, reinforcement learning, signal enhancement, model averaging and selection, computer vision, semiconductor design, population biology, dynamic Bayesian networks, and time series analysis. This will be of great value to students, researchers and practicioners, who have some basic knowledge of probability. Arnaud Doucet received the Ph. D. degree from the University of Paris- XI Orsay in 1997. From 1998 to 2000, he conducted research at the Signal Processing Group of Cambridge University, UK. He is currently an assistant professor at the Department of Electrical Engineering of Melbourne University, Australia. His research interests include Bayesian statistics, dynamic models and Monte Carlo methods. Nando de Freitas obtained a Ph.D. degree in information engineering from Cambridge University in 1999. He is presently a research associate with the artificial intelligence group of the University of California at Berkeley. His main research interests are in Bayesian statistics and the application of on-line and batch Monte Carlo methods to machine learning. En ligne : https://link.springer.com/book/10.1007/978-1-4757-3437-9 Sequential Monte Carlo methods in practice [livre] / Doucet, A.(Ed.), Éditeur scientifique ; de Freitas, N.(Ed.), Éditeur scientifique ; Gordon, N.(Ed.), Éditeur scientifique . - New York : Springer, 2001 . - 581 p.. - (Statistics for engineering and information science) .
ISBN : 978-0-387-95146-1
Langues : Anglais (eng)
Mots-clés : Monte Carlo method Statistical methods Résumé : Le site éditeur indique : Monte Carlo methods are revolutionising the on-line analysis of data in fields as diverse as financial modelling, target tracking and computer vision. These methods, appearing under the names of bootstrap filters, condensation, optimal Monte Carlo filters, particle filters and survial of the fittest, have made it possible to solve numerically many complex, non-standarard problems that were previously intractable. This book presents the first comprehensive treatment of these techniques, including convergence results and applications to tracking, guidance, automated target recognition, aircraft navigation, robot navigation, econometrics, financial modelling, neural networks,optimal control, optimal filtering, communications, reinforcement learning, signal enhancement, model averaging and selection, computer vision, semiconductor design, population biology, dynamic Bayesian networks, and time series analysis. This will be of great value to students, researchers and practicioners, who have some basic knowledge of probability. Arnaud Doucet received the Ph. D. degree from the University of Paris- XI Orsay in 1997. From 1998 to 2000, he conducted research at the Signal Processing Group of Cambridge University, UK. He is currently an assistant professor at the Department of Electrical Engineering of Melbourne University, Australia. His research interests include Bayesian statistics, dynamic models and Monte Carlo methods. Nando de Freitas obtained a Ph.D. degree in information engineering from Cambridge University in 1999. He is presently a research associate with the artificial intelligence group of the University of California at Berkeley. His main research interests are in Bayesian statistics and the application of on-line and batch Monte Carlo methods to machine learning. En ligne : https://link.springer.com/book/10.1007/978-1-4757-3437-9 Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 69098 DOU_11_69098 Livre Salle des ouvrages 11_Mathématiques Sorti jusqu'au 25/05/2043
Titre : Bayesian models for categorical data Type de document : livre Auteurs : Congdon, P. Editeur : Chichester, U.K. : Wiley Année de publication : 2005 Collection : Wiley series in probability and statistics Importance : 425 p. ISBN/ISSN/EAN : 978-0-470-09237-8 Langues : Anglais (eng) Mots-clés : Bayesian statistical decision theory Markov processes Monte Carlo method Multivariate analysis Résumé : The use of Bayesian methods for the analysis of data has grown substantially in areas as diverse as applied statistics, psychology, economics and medical science. Bayesian Methods for Categorical Data sets out to demystify modern Bayesian methods, making them accessible to students and researchers alike. Emphasizing the use of statistical computing and applied data analysis, this book provides a comprehensive introduction to Bayesian methods of categorical outcomes. * Reviews recent Bayesian methodology for categorical outcomes (binary, count and multinomial data). * Considers missing data models techniques and non-standard models (ZIP and negative binomial). * Evaluates time series and spatio-temporal models for discrete data. * Features discussion of univariate and multivariate techniques. * Provides a set of downloadable worked examples with documented WinBUGS code, available from an ftp site. The author's previous 2 bestselling titles provided a comprehensive introduction to the theory and application of Bayesian models. Bayesian Models for Categorical Data continues to build upon this foundation by developing their application to categorical, or discrete data - one of the most common types of data available. The author's clear and logical approach makes the book accessible to a wide range of students and practitioners, including those dealing with categorical data in medicine, sociology, psychology and epidemiology. [Description éditeur] Note de contenu : Hbk; En ligne : http://www.loc.gov/catdir/toc/ecip058/2005005158.html Bayesian models for categorical data [livre] / Congdon, P. . - Chichester, U.K. : Wiley, 2005 . - 425 p.. - (Wiley series in probability and statistics) .
ISBN : 978-0-470-09237-8
Langues : Anglais (eng)
Mots-clés : Bayesian statistical decision theory Markov processes Monte Carlo method Multivariate analysis Résumé : The use of Bayesian methods for the analysis of data has grown substantially in areas as diverse as applied statistics, psychology, economics and medical science. Bayesian Methods for Categorical Data sets out to demystify modern Bayesian methods, making them accessible to students and researchers alike. Emphasizing the use of statistical computing and applied data analysis, this book provides a comprehensive introduction to Bayesian methods of categorical outcomes. * Reviews recent Bayesian methodology for categorical outcomes (binary, count and multinomial data). * Considers missing data models techniques and non-standard models (ZIP and negative binomial). * Evaluates time series and spatio-temporal models for discrete data. * Features discussion of univariate and multivariate techniques. * Provides a set of downloadable worked examples with documented WinBUGS code, available from an ftp site. The author's previous 2 bestselling titles provided a comprehensive introduction to the theory and application of Bayesian models. Bayesian Models for Categorical Data continues to build upon this foundation by developing their application to categorical, or discrete data - one of the most common types of data available. The author's clear and logical approach makes the book accessible to a wide range of students and practitioners, including those dealing with categorical data in medicine, sociology, psychology and epidemiology. [Description éditeur] Note de contenu : Hbk; En ligne : http://www.loc.gov/catdir/toc/ecip058/2005005158.html Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 68368 Con_11_68368 Livre Salle des ouvrages 11_Mathématiques Disponible