### Resultado de la búsqueda

###
**109** búsqueda de la palabra clave **
'Programs'**

Refinar búsqueda Generar rss de la búsqueda
Link de la búsqueda

Título : Applied Multidimensional Scaling Tipo de documento: documento electrónico Autores: Ingwer Borg ; SpringerLink (Online service) ; Patrick J. F. Groenen ; Patrick Mair Editorial: Berlin, Heidelberg : Springer Berlin Heidelberg Fecha de publicación: 2013 Otro editor: Imprint: Springer Colección: SpringerBriefs in Statistics, ISSN 2191-544X Número de páginas: IX, 113 p. 59 illus Il.: online resource ISBN/ISSN/DL: 978-3-642-31848-1 Idioma : Inglés ( eng)Materias: Estadística Palabras clave: Statistics Mathematics Visualization Social sciences Psychology and Computing/Statistics Programs Psychology, general for Science, Behavorial Education, Public Policy, Law in the Humanities Sciences Clasificación: 519.2 Probabilidad y estadística matemática Resumen: This book introduces MDS as a psychological model and as a data analysis technique for the applied researcher. It also discusses, in detail, how to use two MDS programs, Proxscal (a module of SPSS) and Smacof (an R-package). The book is unique in its orientation on the applied researcher, whose primary interest is in using MDS as a tool to build substantive theories. This is done by emphasizing practical issues (such as evaluating model fit), by presenting ways to enforce theoretical expectations on the MDS solution, and by discussing typical mistakes that MDS users tend to make. The primary audience of this book are psychologists, social scientists, and market researchers. No particular background knowledge is required, beyond a basic knowledge of statistics Nota de contenido: First Steps -- The Purpose of MDS -- The Goodness of an MDS Solution -- Proximities -- Variants of Different MDS Models -- Confirmatory MDS -- Typical Mistakes in MDS -- MDS Algorithms -- Computer Programs for MDS En línea: http://dx.doi.org/10.1007/978-3-642-31848-1 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=32519 Applied Multidimensional Scaling [documento electrónico] / Ingwer Borg ; SpringerLink (Online service) ; Patrick J. F. Groenen ; Patrick Mair . - Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013 . - IX, 113 p. 59 illus : online resource. - (SpringerBriefs in Statistics, ISSN 2191-544X) .ISBN: 978-3-642-31848-1

Idioma : Inglés (eng)

Materias: Estadística Palabras clave: Statistics Mathematics Visualization Social sciences Psychology and Computing/Statistics Programs Psychology, general for Science, Behavorial Education, Public Policy, Law in the Humanities Sciences Clasificación: 519.2 Probabilidad y estadística matemática Resumen: This book introduces MDS as a psychological model and as a data analysis technique for the applied researcher. It also discusses, in detail, how to use two MDS programs, Proxscal (a module of SPSS) and Smacof (an R-package). The book is unique in its orientation on the applied researcher, whose primary interest is in using MDS as a tool to build substantive theories. This is done by emphasizing practical issues (such as evaluating model fit), by presenting ways to enforce theoretical expectations on the MDS solution, and by discussing typical mistakes that MDS users tend to make. The primary audience of this book are psychologists, social scientists, and market researchers. No particular background knowledge is required, beyond a basic knowledge of statistics Nota de contenido: First Steps -- The Purpose of MDS -- The Goodness of an MDS Solution -- Proximities -- Variants of Different MDS Models -- Confirmatory MDS -- Typical Mistakes in MDS -- MDS Algorithms -- Computer Programs for MDS En línea: http://dx.doi.org/10.1007/978-3-642-31848-1 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=32519 ## Ejemplares

Signatura Medio Ubicación Sub-localización Sección Estado ningún ejemplar

Título : Introduction to Stochastic Programming Tipo de documento: documento electrónico Autores: John R. Birge ; SpringerLink (Online service) ; François Louveaux Editorial: New York, NY : Springer New York Fecha de publicación: 2011 Colección: Springer Series in Operations Research and Financial Engineering, ISSN 1431-8598 Número de páginas: XXV, 485 p. 44 illus Il.: online resource ISBN/ISSN/DL: 978-1-4614-0237-4 Idioma : Inglés ( eng)Palabras clave: Mathematics Mathematical optimization Operations research Management science Statistics Research, Science and Computing/Statistics Programs Optimization Clasificación: 51 Matemáticas Resumen: The aim of stochastic programming is to find optimal decisions in problems which involve uncertain data. This field is currently developing rapidly with contributions from many disciplines including operations research, mathematics, and probability. At the same time, it is now being applied in a wide variety of subjects ranging from agriculture to financial planning and from industrial engineering to computer networks. This textbook provides a first course in stochastic programming suitable for students with a basic knowledge of linear programming, elementary analysis, and probability. The authors aim to present a broad overview of the main themes and methods of the subject. Its prime goal is to help students develop an intuition on how to model uncertainty into mathematical problems, what uncertainty changes bring to the decision process, and what techniques help to manage uncertainty in solving the problems. In this extensively updated new edition there is more material on methods and examples including several new approaches for discrete variables, new results on risk measures in modeling and Monte Carlo sampling methods, a new chapter on relationships to other methods including approximate dynamic programming, robust optimization and online methods. The book is highly illustrated with chapter summaries and many examples and exercises. Students, researchers and practitioners in operations research and the optimization area will find it particularly of interest. Review of First Edition: "The discussion on modeling issues, the large number of examples used to illustrate the material, and the breadth of the coverage make 'Introduction to Stochastic Programming' an ideal textbook for the area." (Interfaces, 1998) Nota de contenido: Introduction and Examples -- Uncertainty and Modeling Issues -- Basic Properties and Theory -- The Value of Information and the Stochastic Solution -- Two-Stage Recourse Problems -- Multistage Stochastic Programs -- Stochastic Integer Programs -- Evaluating and Approximating Expectations -- Monte Carlo Methods -- Multistage Approximations -- Sample Distribution Functions -- References En línea: http://dx.doi.org/10.1007/978-1-4614-0237-4 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33217 Introduction to Stochastic Programming [documento electrónico] / John R. Birge ; SpringerLink (Online service) ; François Louveaux . - New York, NY : Springer New York, 2011 . - XXV, 485 p. 44 illus : online resource. - (Springer Series in Operations Research and Financial Engineering, ISSN 1431-8598) .ISBN: 978-1-4614-0237-4

Idioma : Inglés (eng)

Palabras clave: Mathematics Mathematical optimization Operations research Management science Statistics Research, Science and Computing/Statistics Programs Optimization Clasificación: 51 Matemáticas Resumen: The aim of stochastic programming is to find optimal decisions in problems which involve uncertain data. This field is currently developing rapidly with contributions from many disciplines including operations research, mathematics, and probability. At the same time, it is now being applied in a wide variety of subjects ranging from agriculture to financial planning and from industrial engineering to computer networks. This textbook provides a first course in stochastic programming suitable for students with a basic knowledge of linear programming, elementary analysis, and probability. The authors aim to present a broad overview of the main themes and methods of the subject. Its prime goal is to help students develop an intuition on how to model uncertainty into mathematical problems, what uncertainty changes bring to the decision process, and what techniques help to manage uncertainty in solving the problems. In this extensively updated new edition there is more material on methods and examples including several new approaches for discrete variables, new results on risk measures in modeling and Monte Carlo sampling methods, a new chapter on relationships to other methods including approximate dynamic programming, robust optimization and online methods. The book is highly illustrated with chapter summaries and many examples and exercises. Students, researchers and practitioners in operations research and the optimization area will find it particularly of interest. Review of First Edition: "The discussion on modeling issues, the large number of examples used to illustrate the material, and the breadth of the coverage make 'Introduction to Stochastic Programming' an ideal textbook for the area." (Interfaces, 1998) Nota de contenido: Introduction and Examples -- Uncertainty and Modeling Issues -- Basic Properties and Theory -- The Value of Information and the Stochastic Solution -- Two-Stage Recourse Problems -- Multistage Stochastic Programs -- Stochastic Integer Programs -- Evaluating and Approximating Expectations -- Monte Carlo Methods -- Multistage Approximations -- Sample Distribution Functions -- References En línea: http://dx.doi.org/10.1007/978-1-4614-0237-4 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33217 ## Ejemplares

Signatura Medio Ubicación Sub-localización Sección Estado ningún ejemplar

Título : Branch-and-Bound Applications in Combinatorial Data Analysis Tipo de documento: documento electrónico Autores: Michael J. Brusco ; SpringerLink (Online service) ; Stephanie Stahl Editorial: New York, NY : Springer New York Fecha de publicación: 2005 Colección: Statistics and Computing, ISSN 1431-8784 Número de páginas: XII, 222 p Il.: online resource ISBN/ISSN/DL: 978-0-387-28810-9 Idioma : Inglés ( eng)Palabras clave: Mathematics Operations research Decision making Management science Discrete mathematics Statistics and Computing/Statistics Programs Operation Research/Decision Theory Research, Science for Social Science, Behavorial Education, Public Policy, Law Clasificación: 51 Matemáticas Resumen: There are a variety of combinatorial optimization problems that are relevant to the examination of statistical data. Combinatorial problems arise in the clustering of a collection of objects, the seriation (sequencing or ordering) of objects, and the selection of variables for subsequent multivariate statistical analysis such as regression. The options for choosing a solution strategy in combinatorial data analysis can be overwhelming. Because some problems are too large or intractable for an optimal solution strategy, many researchers develop an over-reliance on heuristic methods to solve all combinatorial problems. However, with increasingly accessible computer power and ever-improving methodologies, optimal solution strategies have gained popularity for their ability to reduce unnecessary uncertainty. In this monograph, optimality is attained for nontrivially sized problems via the branch-and-bound paradigm. For many combinatorial problems, branch-and-bound approaches have been proposed and/or developed. However, until now, there has not been a single resource in statistical data analysis to summarize and illustrate available methods for applying the branch-and-bound process. This monograph provides clear explanatory text, illustrative mathematics and algorithms, demonstrations of the iterative process, psuedocode, and well-developed examples for applications of the branch-and-bound paradigm to important problems in combinatorial data analysis. Supplementary material, such as computer programs, are provided on the world wide web. Dr. Brusco is a Professor of Marketing and Operations Research at Florida State University, an editorial board member for the Journal of Classification, and a member of the Board of Directors for the Classification Society of North America. Stephanie Stahl is an author and researcher with years of experience in writing, editing, and quantitative psychology research Nota de contenido: Cluster Analysis—Partitioning -- An Introduction to Branch-and-Bound Methods for Partitioning -- Minimum-Diameter Partitioning -- Minimum Within-Cluster Sums of Dissimilarities Partitioning -- Minimum Within-Cluster Sums of Squares Partitioning -- Multiobjective Partitioning -- Seriation -- to the Branch-and-Bound Paradigm for Seriation -- Seriation—Maximization of a Dominance Index -- Seriation—Maximization of Gradient Indices -- Seriation—Unidimensional Scaling -- Seriation—Multiobjective Seriation -- Variable Selection -- to Branch-and-Bound Methods for Variable Selection -- Variable Selection for Cluster Analysis -- Variable Selection for Regression Analysis En línea: http://dx.doi.org/10.1007/0-387-28810-4 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=35154 Branch-and-Bound Applications in Combinatorial Data Analysis [documento electrónico] / Michael J. Brusco ; SpringerLink (Online service) ; Stephanie Stahl . - New York, NY : Springer New York, 2005 . - XII, 222 p : online resource. - (Statistics and Computing, ISSN 1431-8784) .ISBN: 978-0-387-28810-9

Idioma : Inglés (eng)

Palabras clave: Mathematics Operations research Decision making Management science Discrete mathematics Statistics and Computing/Statistics Programs Operation Research/Decision Theory Research, Science for Social Science, Behavorial Education, Public Policy, Law Clasificación: 51 Matemáticas Resumen: There are a variety of combinatorial optimization problems that are relevant to the examination of statistical data. Combinatorial problems arise in the clustering of a collection of objects, the seriation (sequencing or ordering) of objects, and the selection of variables for subsequent multivariate statistical analysis such as regression. The options for choosing a solution strategy in combinatorial data analysis can be overwhelming. Because some problems are too large or intractable for an optimal solution strategy, many researchers develop an over-reliance on heuristic methods to solve all combinatorial problems. However, with increasingly accessible computer power and ever-improving methodologies, optimal solution strategies have gained popularity for their ability to reduce unnecessary uncertainty. In this monograph, optimality is attained for nontrivially sized problems via the branch-and-bound paradigm. For many combinatorial problems, branch-and-bound approaches have been proposed and/or developed. However, until now, there has not been a single resource in statistical data analysis to summarize and illustrate available methods for applying the branch-and-bound process. This monograph provides clear explanatory text, illustrative mathematics and algorithms, demonstrations of the iterative process, psuedocode, and well-developed examples for applications of the branch-and-bound paradigm to important problems in combinatorial data analysis. Supplementary material, such as computer programs, are provided on the world wide web. Dr. Brusco is a Professor of Marketing and Operations Research at Florida State University, an editorial board member for the Journal of Classification, and a member of the Board of Directors for the Classification Society of North America. Stephanie Stahl is an author and researcher with years of experience in writing, editing, and quantitative psychology research Nota de contenido: Cluster Analysis—Partitioning -- An Introduction to Branch-and-Bound Methods for Partitioning -- Minimum-Diameter Partitioning -- Minimum Within-Cluster Sums of Dissimilarities Partitioning -- Minimum Within-Cluster Sums of Squares Partitioning -- Multiobjective Partitioning -- Seriation -- to the Branch-and-Bound Paradigm for Seriation -- Seriation—Maximization of a Dominance Index -- Seriation—Maximization of Gradient Indices -- Seriation—Unidimensional Scaling -- Seriation—Multiobjective Seriation -- Variable Selection -- to Branch-and-Bound Methods for Variable Selection -- Variable Selection for Cluster Analysis -- Variable Selection for Regression Analysis En línea: http://dx.doi.org/10.1007/0-387-28810-4 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=35154 ## Ejemplares

Signatura Medio Ubicación Sub-localización Sección Estado ningún ejemplar

Título : Data Manipulation with R Tipo de documento: documento electrónico Autores: Phil Spector ; SpringerLink (Online service) Editorial: New York, NY : Springer New York Fecha de publicación: 2008 Colección: Use R!, ISSN 2197-5736 Número de páginas: X, 154 p Il.: online resource ISBN/ISSN/DL: 978-0-387-74731-6 Idioma : Inglés ( eng)Palabras clave: Mathematics Computer mathematics software Probabilities Statistics Probability Theory and Stochastic Processes Computational Numerical Analysis Mathematical Software Computing/Statistics Programs Clasificación: 51 Matemáticas Resumen: Since its inception, R has become one of the preeminent programs for statistical computing and data analysis. The ready availability of the program, along with a wide variety of packages and the supportive R community make R an excellent choice for almost any kind of computing task related to statistics. However, many users, especially those with experience in other languages, do not take advantage of the full power of R. Because of the nature of R, solutions that make sense in other languages may not be very efficient in R. This book presents a wide array of methods applicable for reading data into R, and efficiently manipulating that data. In addition to the built-in functions, a number of readily available packages from CRAN (the Comprehensive R Archive Network) are also covered. All of the methods presented take advantage of the core features of R: vectorization, efficient use of subscripting, and the proper use of the varied functions in R that are provided for common data management tasks. Most experienced R users discover that, especially when working with large data sets, it may be helpful to use other programs, notably databases, in conjunction with R. Accordingly, the use of databases in R is covered in detail, along with methods for extracting data from spreadsheets and datasets created by other programs. Character manipulation, while sometimes overlooked within R, is also covered in detail, allowing problems that are traditionally solved by scripting languages to be carried out entirely within R. For users with experience in other languages, guidelines for the effective use of programming constructs like loops are provided. Since many statistical modeling and graphics functions need their data presented in a data frame, techniques for converting the output of commonly used functions to data frames are provided throughout the book. Using a variety of examples based on data sets included with R, along with easily simulated data sets, the book is recommended to anyone using R who wishes to advance from simple examples to practical real-life data manipulation solutions. Phil Spector is Applications Manager of the Statistical Computing Facility and Adjunct Professor in the Department of Statistics at University of California, Berkeley Nota de contenido: Data in R -- Reading and Writing Data -- R and Databases -- Dates -- Factors -- Subscripting -- Character Manipulation -- Data Aggregation -- Reshaping Data En línea: http://dx.doi.org/10.1007/978-0-387-74731-6 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34197 Data Manipulation with R [documento electrónico] / Phil Spector ; SpringerLink (Online service) . - New York, NY : Springer New York, 2008 . - X, 154 p : online resource. - (Use R!, ISSN 2197-5736) .ISBN: 978-0-387-74731-6

Idioma : Inglés (eng)

Palabras clave: Mathematics Computer mathematics software Probabilities Statistics Probability Theory and Stochastic Processes Computational Numerical Analysis Mathematical Software Computing/Statistics Programs Clasificación: 51 Matemáticas Resumen: Since its inception, R has become one of the preeminent programs for statistical computing and data analysis. The ready availability of the program, along with a wide variety of packages and the supportive R community make R an excellent choice for almost any kind of computing task related to statistics. However, many users, especially those with experience in other languages, do not take advantage of the full power of R. Because of the nature of R, solutions that make sense in other languages may not be very efficient in R. This book presents a wide array of methods applicable for reading data into R, and efficiently manipulating that data. In addition to the built-in functions, a number of readily available packages from CRAN (the Comprehensive R Archive Network) are also covered. All of the methods presented take advantage of the core features of R: vectorization, efficient use of subscripting, and the proper use of the varied functions in R that are provided for common data management tasks. Most experienced R users discover that, especially when working with large data sets, it may be helpful to use other programs, notably databases, in conjunction with R. Accordingly, the use of databases in R is covered in detail, along with methods for extracting data from spreadsheets and datasets created by other programs. Character manipulation, while sometimes overlooked within R, is also covered in detail, allowing problems that are traditionally solved by scripting languages to be carried out entirely within R. For users with experience in other languages, guidelines for the effective use of programming constructs like loops are provided. Since many statistical modeling and graphics functions need their data presented in a data frame, techniques for converting the output of commonly used functions to data frames are provided throughout the book. Using a variety of examples based on data sets included with R, along with easily simulated data sets, the book is recommended to anyone using R who wishes to advance from simple examples to practical real-life data manipulation solutions. Phil Spector is Applications Manager of the Statistical Computing Facility and Adjunct Professor in the Department of Statistics at University of California, Berkeley Nota de contenido: Data in R -- Reading and Writing Data -- R and Databases -- Dates -- Factors -- Subscripting -- Character Manipulation -- Data Aggregation -- Reshaping Data En línea: http://dx.doi.org/10.1007/978-0-387-74731-6 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34197 ## Ejemplares

Signatura Medio Ubicación Sub-localización Sección Estado ningún ejemplar

Título : Introducing Monte Carlo Methods with R Tipo de documento: documento electrónico Autores: Christian P. Robert ; SpringerLink (Online service) ; George Casella Editorial: New York, NY : Springer New York Fecha de publicación: 2010 Colección: Use R Número de páginas: XX, 284 p Il.: online resource ISBN/ISSN/DL: 978-1-4419-1576-4 Idioma : Inglés ( eng)Palabras clave: Mathematics Mathematical statistics Computer simulation mathematics Probabilities Statistics Applied Engineering Probability Theory and Stochastic Processes Computational Numerical Analysis Computing/Statistics Programs Simulation Modeling in Science Appl.Mathematics/Computational Methods of Clasificación: 51 Matemáticas Resumen: Computational techniques based on simulation have now become an essential part of the statistician's toolbox. It is thus crucial to provide statisticians with a practical understanding of those methods, and there is no better way to develop intuition and skills for simulation than to use simulation to solve statistical problems. Introducing Monte Carlo Methods with R covers the main tools used in statistical simulation from a programmer's point of view, explaining the R implementation of each simulation technique and providing the output for better understanding and comparison. While this book constitutes a comprehensive treatment of simulation methods, the theoretical justification of those methods has been considerably reduced, compared with Robert and Casella (2004). Similarly, the more exploratory and less stable solutions are not covered here. This book does not require a preliminary exposure to the R programming language or to Monte Carlo methods, nor an advanced mathematical background. While many examples are set within a Bayesian framework, advanced expertise in Bayesian statistics is not required. The book covers basic random generation algorithms, Monte Carlo techniques for integration and optimization, convergence diagnoses, Markov chain Monte Carlo methods, including Metropolis {Hastings and Gibbs algorithms, and adaptive algorithms. All chapters include exercises and all R programs are available as an R package called mcsm. The book appeals to anyone with a practical interest in simulation methods but no previous exposure. It is meant to be useful for students and practitioners in areas such as statistics, signal processing, communications engineering, control theory, econometrics, finance and more. The programming parts are introduced progressively to be accessible to any reader. Christian P. Robert is Professor of Statistics at Université Paris Dauphine, and Head of the Statistics Laboratory of CREST, both in Paris, France. He has authored more than 150 papers in applied probability, Bayesian statistics and simulation methods. He is a fellow of the Institute of Mathematical Statistics and the recipient of an IMS Medallion. He has authored eight other books, including The Bayesian Choice which received the ISBA DeGroot Prize in 2004, Monte Carlo Statistical Methods with George Casella, and Bayesian Core with Jean-Michel Marin. He has served as Joint Editor of the Journal of the Royal Statistical Society Series B, as well as an associate editor for most major statistical journals, and was the 2008 ISBA President. George Casella is Distinguished Professor in the Department of Statistics at the University of Florida. He is active in both theoretical and applied statistics, is a fellow of the Institute of Mathematical Statistics and the American Statistical Association, and a Foreign Member of the Spanish Royal Academy of Sciences. He has served as Theory and Methods Editor of the Journal of the American Statistical Association, as Executive Editor of Statistical Science, and as Joint Editor of the Journal of the Royal Statistical Society Series B. In addition to books with Christian Robert, he has written Variance Components, 1992, with S.R. Searle and C.E. McCulloch; Statistical Inference, Second Edition, 2001, with Roger Berger; and Theory of Point Estimation, Second Edition, 1998, with Erich Lehmann. His latest book is Statistical Design 2008 Nota de contenido: Basic R Programming -- Random Variable Generation -- Monte Carlo Integration -- Controlling and Accelerating Convergence -- Monte Carlo Optimization -- Metropolis#x2013;Hastings Algorithms -- Gibbs Samplers -- Convergence Monitoring and Adaptation for MCMC Algorithms En línea: http://dx.doi.org/10.1007/978-1-4419-1576-4 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33597 Introducing Monte Carlo Methods with R [documento electrónico] / Christian P. Robert ; SpringerLink (Online service) ; George Casella . - New York, NY : Springer New York, 2010 . - XX, 284 p : online resource. - (Use R) .ISBN: 978-1-4419-1576-4

Idioma : Inglés (eng)

Palabras clave: Mathematics Mathematical statistics Computer simulation mathematics Probabilities Statistics Applied Engineering Probability Theory and Stochastic Processes Computational Numerical Analysis Computing/Statistics Programs Simulation Modeling in Science Appl.Mathematics/Computational Methods of Clasificación: 51 Matemáticas Resumen: Computational techniques based on simulation have now become an essential part of the statistician's toolbox. It is thus crucial to provide statisticians with a practical understanding of those methods, and there is no better way to develop intuition and skills for simulation than to use simulation to solve statistical problems. Introducing Monte Carlo Methods with R covers the main tools used in statistical simulation from a programmer's point of view, explaining the R implementation of each simulation technique and providing the output for better understanding and comparison. While this book constitutes a comprehensive treatment of simulation methods, the theoretical justification of those methods has been considerably reduced, compared with Robert and Casella (2004). Similarly, the more exploratory and less stable solutions are not covered here. This book does not require a preliminary exposure to the R programming language or to Monte Carlo methods, nor an advanced mathematical background. While many examples are set within a Bayesian framework, advanced expertise in Bayesian statistics is not required. The book covers basic random generation algorithms, Monte Carlo techniques for integration and optimization, convergence diagnoses, Markov chain Monte Carlo methods, including Metropolis {Hastings and Gibbs algorithms, and adaptive algorithms. All chapters include exercises and all R programs are available as an R package called mcsm. The book appeals to anyone with a practical interest in simulation methods but no previous exposure. It is meant to be useful for students and practitioners in areas such as statistics, signal processing, communications engineering, control theory, econometrics, finance and more. The programming parts are introduced progressively to be accessible to any reader. Christian P. Robert is Professor of Statistics at Université Paris Dauphine, and Head of the Statistics Laboratory of CREST, both in Paris, France. He has authored more than 150 papers in applied probability, Bayesian statistics and simulation methods. He is a fellow of the Institute of Mathematical Statistics and the recipient of an IMS Medallion. He has authored eight other books, including The Bayesian Choice which received the ISBA DeGroot Prize in 2004, Monte Carlo Statistical Methods with George Casella, and Bayesian Core with Jean-Michel Marin. He has served as Joint Editor of the Journal of the Royal Statistical Society Series B, as well as an associate editor for most major statistical journals, and was the 2008 ISBA President. George Casella is Distinguished Professor in the Department of Statistics at the University of Florida. He is active in both theoretical and applied statistics, is a fellow of the Institute of Mathematical Statistics and the American Statistical Association, and a Foreign Member of the Spanish Royal Academy of Sciences. He has served as Theory and Methods Editor of the Journal of the American Statistical Association, as Executive Editor of Statistical Science, and as Joint Editor of the Journal of the Royal Statistical Society Series B. In addition to books with Christian Robert, he has written Variance Components, 1992, with S.R. Searle and C.E. McCulloch; Statistical Inference, Second Edition, 2001, with Roger Berger; and Theory of Point Estimation, Second Edition, 1998, with Erich Lehmann. His latest book is Statistical Design 2008 Nota de contenido: Basic R Programming -- Random Variable Generation -- Monte Carlo Integration -- Controlling and Accelerating Convergence -- Monte Carlo Optimization -- Metropolis#x2013;Hastings Algorithms -- Gibbs Samplers -- Convergence Monitoring and Adaptation for MCMC Algorithms En línea: http://dx.doi.org/10.1007/978-1-4419-1576-4 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33597 ## Ejemplares

Signatura Medio Ubicación Sub-localización Sección Estado ningún ejemplar PermalinkPermalinkPermalinkPermalinkPermalink