Monte Carlo Statistical Methods

Author: Christian Robert,George Casella

Publisher: Springer Science & Business Media

ISBN: 1475730713

Category: Mathematics

Page: 509

View: 2767

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We have sold 4300 copies worldwide of the first edition (1999). This new edition contains five completely new chapters covering new developments.
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A First Course in Bayesian Statistical Methods

Author: Peter D. Hoff

Publisher: Springer Science & Business Media

ISBN: 9780387924076

Category: Mathematics

Page: 272

View: 947

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A self-contained introduction to probability, exchangeability and Bayes’ rule provides a theoretical understanding of the applied material. Numerous examples with R-code that can be run "as-is" allow the reader to perform the data analyses themselves. The development of Monte Carlo and Markov chain Monte Carlo methods in the context of data analysis examples provides motivation for these computational methods.
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Case Studies in Bayesian Statistical Modelling and Analysis

Author: Clair L. Alston,Kerrie L. Mengersen,Anthony N. Pettitt

Publisher: John Wiley & Sons

ISBN: 1118394321

Category: Mathematics

Page: 504

View: 5516

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Provides an accessible foundation to Bayesian analysis usingreal world models This book aims to present an introduction to Bayesian modellingand computation, by considering real case studies drawn fromdiverse fields spanning ecology, health, genetics and finance. Eachchapter comprises a description of the problem, the correspondingmodel, the computational method, results and inferences as well asthe issues that arise in the implementation of theseapproaches. Case Studies in Bayesian Statistical Modelling andAnalysis: Illustrates how to do Bayesian analysis in a clear and concisemanner using real-world problems. Each chapter focuses on a real-world problem and describes theway in which the problem may be analysed using Bayesianmethods. Features approaches that can be used in a wide area ofapplication, such as, health, the environment, genetics,information science, medicine, biology, industry and remotesensing. Case Studies in Bayesian Statistical Modelling andAnalysis is aimed at statisticians, researchers andpractitioners who have some expertise in statistical modelling andanalysis, and some understanding of the basics of Bayesianstatistics, but little experience in its application. Graduatestudents of statistics and biostatistics will also find this bookbeneficial.
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Monte Carlo and Quasi-Monte Carlo Methods 2008

Author: Pierre L' Ecuyer,Art B. Owen

Publisher: Springer Science & Business Media

ISBN: 9783642041075

Category: Mathematics

Page: 672

View: 2749

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This book represents the refereed proceedings of the Eighth International Conference on Monte Carlo (MC)and Quasi-Monte Carlo (QMC) Methods in Scientific Computing, held in Montreal (Canada) in July 2008. It covers the latest theoretical developments as well as important applications of these methods in different areas. It contains two tutorials, eight invited articles, and 32 carefully selected articles based on the 135 contributed presentations made at the conference. This conference is a major event in Monte Carlo methods and is the premiere event for quasi-Monte Carlo and its combination with Monte Carlo. This series of proceedings volumes is the primary outlet for quasi-Monte Carlo research.
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Monte Carlo and Quasi-Monte Carlo Sampling

Author: Christiane Lemieux

Publisher: Springer Science & Business Media

ISBN: 038778165X

Category: Mathematics

Page: 373

View: 3125

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Quasi–Monte Carlo methods have become an increasingly popular alternative to Monte Carlo methods over the last two decades. Their successful implementation on practical problems, especially in finance, has motivated the development of several new research areas within this field to which practitioners and researchers from various disciplines currently contribute. This book presents essential tools for using quasi–Monte Carlo sampling in practice. The first part of the book focuses on issues related to Monte Carlo methods—uniform and non-uniform random number generation, variance reduction techniques—but the material is presented to prepare the readers for the next step, which is to replace the random sampling inherent to Monte Carlo by quasi–random sampling. The second part of the book deals with this next step. Several aspects of quasi-Monte Carlo methods are covered, including constructions, randomizations, the use of ANOVA decompositions, and the concept of effective dimension. The third part of the book is devoted to applications in finance and more advanced statistical tools like Markov chain Monte Carlo and sequential Monte Carlo, with a discussion of their quasi–Monte Carlo counterpart. The prerequisites for reading this book are a basic knowledge of statistics and enough mathematical maturity to follow through the various techniques used throughout the book. This text is aimed at graduate students in statistics, management science, operations research, engineering, and applied mathematics. It should also be useful to practitioners who want to learn more about Monte Carlo and quasi–Monte Carlo methods and researchers interested in an up-to-date guide to these methods.
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Monte-Carlo Methods and Stochastic Processes

From Linear to Non-Linear

Author: Emmanuel Gobet

Publisher: CRC Press

ISBN: 149874625X

Category: Mathematics

Page: 310

View: 9155

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Developed from the author’s course at the Ecole Polytechnique, Monte-Carlo Methods and Stochastic Processes: From Linear to Non-Linear focuses on the simulation of stochastic processes in continuous time and their link with partial differential equations (PDEs). It covers linear and nonlinear problems in biology, finance, geophysics, mechanics, chemistry, and other application areas. The text also thoroughly develops the problem of numerical integration and computation of expectation by the Monte-Carlo method. The book begins with a history of Monte-Carlo methods and an overview of three typical Monte-Carlo problems: numerical integration and computation of expectation, simulation of complex distributions, and stochastic optimization. The remainder of the text is organized in three parts of progressive difficulty. The first part presents basic tools for stochastic simulation and analysis of algorithm convergence. The second part describes Monte-Carlo methods for the simulation of stochastic differential equations. The final part discusses the simulation of non-linear dynamics.
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Essentials of Monte Carlo Simulation

Statistical Methods for Building Simulation Models

Author: Nick T. Thomopoulos

Publisher: Springer Science & Business Media

ISBN: 1461460220

Category: Mathematics

Page: 174

View: 1811

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Essentials of Monte Carlo Simulation focuses on the fundamentals of Monte Carlo methods using basic computer simulation techniques. The theories presented in this text deal with systems that are too complex to solve analytically. As a result, readers are given a system of interest and constructs using computer code, as well as algorithmic models to emulate how the system works internally. After the models are run several times, in a random sample way, the data for each output variable(s) of interest is analyzed by ordinary statistical methods. This book features 11 comprehensive chapters, and discusses such key topics as random number generators, multivariate random variates, and continuous random variates. Over 100 numerical examples are presented as part of the appendix to illustrate useful real world applications. The text also contains an easy to read presentation with minimal use of difficult mathematical concepts. Very little has been published in the area of computer Monte Carlo simulation methods, and this book will appeal to students and researchers in the fields of Mathematics and Statistics.
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AMSTAT News

Author: American Statistical Association

Publisher: N.A

ISBN: N.A

Category: Statistics

Page: N.A

View: 7579

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Handbook of Sea-Level Research

Author: Ian Shennan,Antony J. Long,Benjamin P. Horton

Publisher: John Wiley & Sons

ISBN: 1118452577

Category: Science

Page: 600

View: 3518

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Measuring sea-level change – be that rise or fall – isone of the most pressing scientific goals of our time and requiresrobust scientific approaches and techniques. This Handbookaims to provide a practical guide to readers interested in thischallenge, from the initial design of research approaches throughto the practical issues of data collection and interpretation froma diverse range of coastal environments. Building on thirtyyears of international research, the Handbook comprises 38 chaptersthat are authored by leading experts from around the world. The Handbook will be an important resource to scientists interestedand involved in understanding sea-level changes across a broadrange of disciplines, policy makers wanting to appreciate ourcurrent state of knowledge of sea-level change over differenttimescales, and many teachers at the university level, as well asadvanced-level undergraduates and postgraduate research students,wanting to learn more about sea-level change. Additional resources for this book can be found at: ahref="http://www.wiley.com/go/shennan/sealevel"www.wiley.com\go\shennan\sealevel/a
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