monte carlo statistical methods springer texts in statistics

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Monte Carlo Statistical Methods

Author : Christian Robert
ISBN : 9781475741452
Genre : Mathematics
File Size : 63. 56 MB
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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.

A First Course In Bayesian Statistical Methods

Author : Peter D. Hoff
ISBN : 0387924078
Genre : Mathematics
File Size : 90. 97 MB
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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.

Case Studies In Bayesian Statistical Modelling And Analysis

Author : Clair L. Alston
ISBN : 9781118394328
Genre : Mathematics
File Size : 52. 54 MB
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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.

Monte Carlo And Quasi Monte Carlo Sampling

Author : Christiane Lemieux
ISBN : 9780387781655
Genre : Mathematics
File Size : 81. 59 MB
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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.

Monte Carlo Methods And Stochastic Processes

Author : Emmanuel Gobet
ISBN : 9781498746250
Genre : Mathematics
File Size : 64. 88 MB
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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.

Essentials Of Monte Carlo Simulation

Author : Nick T. Thomopoulos
ISBN : 9781461460220
Genre : Mathematics
File Size : 46. 1 MB
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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.

Handbook Of Sea Level Research

Author : Ian Shennan
ISBN : 9781118452578
Genre : Science
File Size : 60. 19 MB
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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=""\go\shennan\sealevel/a

Statistical Design

Author : George Casella
ISBN : 9780387759647
Genre : Mathematics
File Size : 77. 13 MB
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Although statistical design is one of the oldest branches of statistics, its importance is ever increasing, especially in the face of the data flood that often faces statisticians. It is important to recognize the appropriate design, and to understand how to effectively implement it, being aware that the default settings from a computer package can easily provide an incorrect analysis. The goal of this book is to describe the principles that drive good design, paying attention to both the theoretical background and the problems arising from real experimental situations. Designs are motivated through actual experiments, ranging from the timeless agricultural randomized complete block, to microarray experiments, which naturally lead to split plot designs and balanced incomplete blocks. George Casella is Distinguished Professor in the Department of Statistics at the University of Florida. He is active in many aspects of statistics, having contributed to theoretical statistics in the areas of decision theory and statistical confidence, to environmental statistics, and has more recently concentrated efforts in statistical genomics. He also maintains active research interests in the theory and application of Monte Carlo and other computationally intensive methods. He is listed as an ISI "Highly Cited Researcher." In other capacities, Professor Casella has served as Theory and Methods Editor of the Journal of the American Statistical Association, 1996-1999, Executive Editor of Statistical Science, 2001-2004, and Co-Editor of the Journal of the Royal Statistical Society, Series B, 2009-2012. He has served on the Board of Mathematical Sciences of the National Research Council, 1999-2003, and many committees of both the American Statistical Association and the Institute of Mathematical Statistics. Professor Casella has co-authored five textbooks: Variance Components, 1992; Theory of Point Estimation, Second Edition, 1998; Monte Carlo Statistical Methods, Second Edition, 2004; Statistical Inference, Second Edition, 2001, and Statistical Genomics of Complex Traits, 2007.

Amstat News

Author :
ISBN : UOM:39015085193624
Genre : Statistics
File Size : 41. 30 MB
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Random Number Generation And Monte Carlo Methods

Author : James E. Gentle
ISBN : 9780387216102
Genre : Computers
File Size : 46. 83 MB
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Monte Carlo simulation has become one of the most important tools in all fields of science. Simulation methodology relies on a good source of numbers that appear to be random. These "pseudorandom" numbers must pass statistical tests just as random samples would. Methods for producing pseudorandom numbers and transforming those numbers to simulate samples from various distributions are among the most important topics in statistical computing. This book surveys techniques of random number generation and the use of random numbers in Monte Carlo simulation. The book covers basic principles, as well as newer methods such as parallel random number generation, nonlinear congruential generators, quasi Monte Carlo methods, and Markov chain Monte Carlo. The best methods for generating random variates from the standard distributions are presented, but also general techniques useful in more complicated models and in novel settings are described. The emphasis throughout the book is on practical methods that work well in current computing environments. The book includes exercises and can be used as a test or supplementary text for various courses in modern statistics. It could serve as the primary test for a specialized course in statistical computing, or as a supplementary text for a course in computational statistics and other areas of modern statistics that rely on simulation. The book, which covers recent developments in the field, could also serve as a useful reference for practitioners. Although some familiarity with probability and statistics is assumed, the book is accessible to a broad audience. The second edition is approximately 50% longer than the first edition. It includes advances in methods for parallel random number generation, universal methods for generation of nonuniform variates, perfect sampling, and software for random number generation.

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