**Author**: Christian Robert,George Casella

**Publisher:** Springer Science & Business Media

**ISBN:** 1441915753

**Category:** Computers

**Page:** 284

**View:** 2136

Skip to content
# Free eBooks PDF

## Introducing Monte Carlo Methods with R

This book 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.
## Introducing Monte Carlo Methods with R

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.
## Introducing Monte Carlo Methods with R

## Introduction to Probability Simulation and Gibbs Sampling with R

The first seven chapters use R for probability simulation and computation, including random number generation, numerical and Monte Carlo integration, and finding limiting distributions of Markov Chains with both discrete and continuous states. Applications include coverage probabilities of binomial confidence intervals, estimation of disease prevalence from screening tests, parallel redundancy for improved reliability of systems, and various kinds of genetic modeling. These initial chapters can be used for a non-Bayesian course in the simulation of applied probability models and Markov Chains. Chapters 8 through 10 give a brief introduction to Bayesian estimation and illustrate the use of Gibbs samplers to find posterior distributions and interval estimates, including some examples in which traditional methods do not give satisfactory results. WinBUGS software is introduced with a detailed explanation of its interface and examples of its use for Gibbs sampling for Bayesian estimation. No previous experience using R is required. An appendix introduces R, and complete R code is included for almost all computational examples and problems (along with comments and explanations). Noteworthy features of the book are its intuitive approach, presenting ideas with examples from biostatistics, reliability, and other fields; its large number of figures; and its extraordinarily large number of problems (about a third of the pages), ranging from simple drill to presentation of additional topics. Hints and answers are provided for many of the problems. These features make the book ideal for students of statistics at the senior undergraduate and at the beginning graduate levels.
## Monte Carlo Simulation and Resampling Methods for Social Science

Taking the topics of a quantitative methodology course and illustrating them through Monte Carlo simulation, Monte Carlo Simulation and Resampling Methods for Social Science, by Thomas M. Carsey and Jeffrey J. Harden, examines abstract principles, such as bias, efficiency, and measures of uncertainty in an intuitive, visual way. Instead of thinking in the abstract about what would happen to a particular estimator "in repeated samples," the book uses simulation to actually create those repeated samples and summarize the results. The book includes basic examples appropriate for readers learning the material for the first time, as well as more advanced examples that a researcher might use to evaluate an estimator he or she was using in an actual research project. The book also covers a wide range of topics related to Monte Carlo simulation, such as resampling methods, simulations of substantive theory, simulation of quantities of interest (QI) from model results, and cross-validation. Complete R code from all examples is provided so readers can replicate every analysis presented using R.
## Bayesian Computation with R

There has been dramatic growth in the development and application of Bayesian inference in statistics. Berger (2000) documents the increase in Bayesian activity by the number of published research articles, the number of books,andtheextensivenumberofapplicationsofBayesianarticlesinapplied disciplines such as science and engineering. One reason for the dramatic growth in Bayesian modeling is the availab- ity of computational algorithms to compute the range of integrals that are necessary in a Bayesian posterior analysis. Due to the speed of modern c- puters, it is now possible to use the Bayesian paradigm to ?t very complex models that cannot be ?t by alternative frequentist methods. To ?t Bayesian models, one needs a statistical computing environment. This environment should be such that one can: write short scripts to de?ne a Bayesian model use or write functions to summarize a posterior distribution use functions to simulate from the posterior distribution construct graphs to illustrate the posterior inference An environment that meets these requirements is the R system. R provides a wide range of functions for data manipulation, calculation, and graphical d- plays. Moreover, it includes a well-developed, simple programming language that users can extend by adding new functions. Many such extensions of the language in the form of packages are easily downloadable from the Comp- hensive R Archive Network (CRAN).
## Essentials of Monte Carlo Simulation

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.
## Explorations in Monte Carlo Methods

Monte Carlo methods are among the most used and useful computational tools available today, providing efficient and practical algorithims to solve a wide range of scientific and engineering problems. Applications covered in this book include optimization, finance, statistical mechanics, birth and death processes, and gambling systems. Explorations in Monte Carlo Methods provides a hands-on approach to learning this subject. Each new idea is carefully motivated by a realistic problem, thus leading from questions to theory via examples and numerical simulations. Programming exercises are integrated throughout the text as the primary vehicle for learning the material. Each chapter ends with a large collection of problems illustrating and directing the material. This book is suitable as a textbook for students of engineering and the sciences, as well as mathematics.
## Monte Carlo Statistical Methods

We have sold 4300 copies worldwide of the first edition (1999). This new edition contains five completely new chapters covering new developments.
## Stochastic Simulation and Monte Carlo Methods

In various scientific and industrial fields, stochastic simulations are taking on a new importance. This is due to the increasing power of computers and practitioners’ aim to simulate more and more complex systems, and thus use random parameters as well as random noises to model the parametric uncertainties and the lack of knowledge on the physics of these systems. The error analysis of these computations is a highly complex mathematical undertaking. Approaching these issues, the authors present stochastic numerical methods and prove accurate convergence rate estimates in terms of their numerical parameters (number of simulations, time discretization steps). As a result, the book is a self-contained and rigorous study of the numerical methods within a theoretical framework. After briefly reviewing the basics, the authors first introduce fundamental notions in stochastic calculus and continuous-time martingale theory, then develop the analysis of pure-jump Markov processes, Poisson processes, and stochastic differential equations. In particular, they review the essential properties of Itô integrals and prove fundamental results on the probabilistic analysis of parabolic partial differential equations. These results in turn provide the basis for developing stochastic numerical methods, both from an algorithmic and theoretical point of view. The book combines advanced mathematical tools, theoretical analysis of stochastic numerical methods, and practical issues at a high level, so as to provide optimal results on the accuracy of Monte Carlo simulations of stochastic processes. It is intended for master and Ph.D. students in the field of stochastic processes and their numerical applications, as well as for physicists, biologists, economists and other professionals working with stochastic simulations, who will benefit from the ability to reliably estimate and control the accuracy of their simulations.
## Introductory Econometrics

This highly accessible and innovative text with supporting web site uses Excel (R) to teach the core concepts of econometrics without advanced mathematics. It enables students to use Monte Carlo simulations in order to understand the data generating process and sampling distribution. Intelligent repetition of concrete examples effectively conveys the properties of the ordinary least squares (OLS) estimator and the nature of heteroskedasticity and autocorrelation. Coverage includes omitted variables, binary response models, basic time series, and simultaneous equations. The authors teach students how to construct their own real-world data sets drawn from the internet, which they can analyze with Excel (R) or with other econometric software. The accompanying web site with text support can be found at www.wabash.edu/econometrics.
## Monte Carlo Methods in Bayesian Computation

Dealing with methods for sampling from posterior distributions and how to compute posterior quantities of interest using Markov chain Monte Carlo (MCMC) samples, this book addresses such topics as improving simulation accuracy, marginal posterior density estimation, estimation of normalizing constants, constrained parameter problems, highest posterior density interval calculations, computation of posterior modes, and posterior computations for proportional hazards models and Dirichlet process models. The authors also discuss model comparisons, including both nested and non-nested models, marginal likelihood methods, ratios of normalizing constants, Bayes factors, the Savage-Dickey density ratio, Stochastic Search Variable Selection, Bayesian Model Averaging, the reverse jump algorithm, and model adequacy using predictive and latent residual approaches. The book presents an equal mixture of theory and applications involving real data, and is intended as a graduate textbook or a reference book for a one-semester course at the advanced masters or Ph.D. level. It will also serve as a useful reference for applied or theoretical researchers as well as practitioners.
## Handbook of Markov Chain Monte Carlo

Since their popularization in the 1990s, Markov chain Monte Carlo (MCMC) methods have revolutionized statistical computing and have had an especially profound impact on the practice of Bayesian statistics. Furthermore, MCMC methods have enabled the development and use of intricate models in an astonishing array of disciplines as diverse as fisheries science and economics. The wide-ranging practical importance of MCMC has sparked an expansive and deep investigation into fundamental Markov chain theory. The Handbook of Markov Chain Monte Carlo provides a reference for the broad audience of developers and users of MCMC methodology interested in keeping up with cutting-edge theory and applications. The first half of the book covers MCMC foundations, methodology, and algorithms. The second half considers the use of MCMC in a variety of practical applications including in educational research, astrophysics, brain imaging, ecology, and sociology. The in-depth introductory section of the book allows graduate students and practicing scientists new to MCMC to become thoroughly acquainted with the basic theory, algorithms, and applications. The book supplies detailed examples and case studies of realistic scientific problems presenting the diversity of methods used by the wide-ranging MCMC community. Those familiar with MCMC methods will find this book a useful refresher of current theory and recent developments.
## Bayesian Essentials with R

This Bayesian modeling book provides a self-contained entry to computational Bayesian statistics. Focusing on the most standard statistical models and backed up by real datasets and an all-inclusive R (CRAN) package called bayess, the book provides an operational methodology for conducting Bayesian inference, rather than focusing on its theoretical and philosophical justifications. Readers are empowered to participate in the real-life data analysis situations depicted here from the beginning. Special attention is paid to the derivation of prior distributions in each case and specific reference solutions are given for each of the models. Similarly, computational details are worked out to lead the reader towards an effective programming of the methods given in the book. In particular, all R codes are discussed with enough detail to make them readily understandable and expandable. Bayesian Essentials with R can be used as a textbook at both undergraduate and graduate levels. It is particularly useful with students in professional degree programs and scientists to analyze data the Bayesian way. The text will also enhance introductory courses on Bayesian statistics. Prerequisites for the book are an undergraduate background in probability and statistics, if not in Bayesian statistics.
## Markov Chains

Markov Chains: Analytic and Monte Carlo Computations introduces the main notions related to Markov chains and provides explanations on how to characterize, simulate, and recognize them. Starting with basic notions, this book leads progressively to advanced and recent topics in the field, allowing the reader to master the main aspects of the classical theory. This book also features: Numerous exercises with solutions as well as extended case studies. A detailed and rigorous presentation of Markov chains with discrete time and state space. An appendix presenting probabilistic notions that are necessary to the reader, as well as giving more advanced measure-theoretic notions.
## The R Book

Hugely successful and popular text presenting an extensive and comprehensive guide for all R users The R language is recognized as one of the most powerful and flexible statistical software packages, enabling users to apply many statistical techniques that would be impossible without such software to help implement such large data sets. R has become an essential tool for understanding and carrying out research. This edition: Features full colour text and extensive graphics throughout. Introduces a clear structure with numbered section headings to help readers locate information more efficiently. Looks at the evolution of R over the past five years. Features a new chapter on Bayesian Analysis and Meta-Analysis. Presents a fully revised and updated bibliography and reference section. Is supported by an accompanying website allowing examples from the text to be run by the user. Praise for the first edition: ‘…if you are an R user or wannabe R user, this text is the one that should be on your shelf. The breadth of topics covered is unsurpassed when it comes to texts on data analysis in R.’ (The American Statistician, August 2008) ‘The High-level software language of R is setting standards in quantitative analysis. And now anybody can get to grips with it thanks to The R Book…’ (Professional Pensions, July 2007)
## Modern Optimization with R

The goal of this book is to gather in a single document the most relevant concepts related to modern optimization methods, showing how such concepts and methods can be addressed using the open source, multi-platform R tool. Modern optimization methods, also known as metaheuristics, are particularly useful for solving complex problems for which no specialized optimization algorithm has been developed. These methods often yield high quality solutions with a more reasonable use of computational resources (e.g. memory and processing effort). Examples of popular modern methods discussed in this book are: simulated annealing; tabu search; genetic algorithms; differential evolution; and particle swarm optimization. This book is suitable for undergraduate and graduate students in Computer Science, Information Technology, and related areas, as well as data analysts interested in exploring modern optimization methods using R.
## Introduction to Scientific Programming and Simulation Using R, Second Edition

Learn How to Program Stochastic Models Highly recommended, the best-selling first edition of Introduction to Scientific Programming and Simulation Using R was lauded as an excellent, easy-to-read introduction with extensive examples and exercises. This second edition continues to introduce scientific programming and stochastic modelling in a clear, practical, and thorough way. Readers learn programming by experimenting with the provided R code and data. The book’s four parts teach: Core knowledge of R and programming concepts How to think about mathematics from a numerical point of view, including the application of these concepts to root finding, numerical integration, and optimisation Essentials of probability, random variables, and expectation required to understand simulation Stochastic modelling and simulation, including random number generation and Monte Carlo integration In a new chapter on systems of ordinary differential equations (ODEs), the authors cover the Euler, midpoint, and fourth-order Runge-Kutta (RK4) schemes for solving systems of first-order ODEs. They compare the numerical efficiency of the different schemes experimentally and show how to improve the RK4 scheme by using an adaptive step size. Another new chapter focuses on both discrete- and continuous-time Markov chains. It describes transition and rate matrices, classification of states, limiting behaviour, Kolmogorov forward and backward equations, finite absorbing chains, and expected hitting times. It also presents methods for simulating discrete- and continuous-time chains as well as techniques for defining the state space, including lumping states and supplementary variables. Building readers’ statistical intuition, Introduction to Scientific Programming and Simulation Using R, Second Edition shows how to turn algorithms into code. It is designed for those who want to make tools, not just use them. The code and data are available for download from CRAN.
## Bayesian Modeling Using WinBUGS

A hands-on introduction to the principles of Bayesian modeling using WinBUGS Bayesian Modeling Using WinBUGS provides an easily accessible introduction to the use of WinBUGS programming techniques in a variety of Bayesian modeling settings. The author provides an accessible treatment of the topic, offering readers a smooth introduction to the principles of Bayesian modeling with detailed guidance on the practical implementation of key principles. The book begins with a basic introduction to Bayesian inference and the WinBUGS software and goes on to cover key topics, including: Markov Chain Monte Carlo algorithms in Bayesian inference Generalized linear models Bayesian hierarchical models Predictive distribution and model checking Bayesian model and variable evaluation Computational notes and screen captures illustrate the use of both WinBUGS as well as R software to apply the discussed techniques. Exercises at the end of each chapter allow readers to test their understanding of the presented concepts and all data sets and code are available on the book's related Web site. Requiring only a working knowledge of probability theory and statistics, Bayesian Modeling Using WinBUGS serves as an excellent book for courses on Bayesian statistics at the upper-undergraduate and graduate levels. It is also a valuable reference for researchers and practitioners in the fields of statistics, actuarial science, medicine, and the social sciences who use WinBUGS in their everyday work.
## The Art of R Programming

A guide to software development using the R programming language covers such topics as closures, recursion, anonymous functions, and debugging techniques.

Just another PDF Download site

Computers

**Author**: Christian Robert,George Casella

**Publisher:** Springer Science & Business Media

**ISBN:** 1441915753

**Category:** Computers

**Page:** 284

**View:** 2136

Mathematics

**Author**: Christian Robert,George Casella

**Publisher:** Springer Science & Business Media

**ISBN:** 1441915761

**Category:** Mathematics

**Page:** 284

**View:** 3190

**Author**: Christian P. Robert,George Casella

**Publisher:** N.A

**ISBN:** 9781441915825

**Category:**

**Page:** 306

**View:** 999

Mathematics

**Author**: Eric A. Suess,Bruce E. Trumbo

**Publisher:** Springer Science & Business Media

**ISBN:** 038740273X

**Category:** Mathematics

**Page:** 307

**View:** 9010

Social Science

**Author**: Thomas M. Carsey,Jeffrey J. Harden

**Publisher:** SAGE Publications

**ISBN:** 1483324923

**Category:** Social Science

**Page:** 304

**View:** 4587

Mathematics

**Author**: Jim Albert

**Publisher:** Springer Science & Business Media

**ISBN:** 0387922989

**Category:** Mathematics

**Page:** 300

**View:** 1047

Mathematics

*Statistical Methods for Building Simulation Models*

**Author**: Nick T. Thomopoulos

**Publisher:** Springer Science & Business Media

**ISBN:** 1461460220

**Category:** Mathematics

**Page:** 174

**View:** 593

Mathematics

**Author**: Ronald W. Shonkwiler,Franklin Mendivil

**Publisher:** Springer Science & Business Media

**ISBN:** 0387878378

**Category:** Mathematics

**Page:** 243

**View:** 1030

Mathematics

**Author**: Christian Robert,George Casella

**Publisher:** Springer Science & Business Media

**ISBN:** 1475741456

**Category:** Mathematics

**Page:** 649

**View:** 1539

Mathematics

*Mathematical Foundations of Stochastic Simulation*

**Author**: Carl Graham,Denis Talay

**Publisher:** Springer Science & Business Media

**ISBN:** 3642393632

**Category:** Mathematics

**Page:** 260

**View:** 3908

Business & Economics

*Using Monte Carlo Simulation with Microsoft Excel*

**Author**: Humberto Barreto,Frank Howland

**Publisher:** Cambridge University Press

**ISBN:** 9780521843195

**Category:** Business & Economics

**Page:** 774

**View:** 1493

Mathematics

**Author**: Ming-Hui Chen,Qi-Man Shao,Joseph G. Ibrahim

**Publisher:** Springer Science & Business Media

**ISBN:** 1461212766

**Category:** Mathematics

**Page:** 387

**View:** 7944

Mathematics

**Author**: Steve Brooks,Andrew Gelman,Galin Jones,Xiao-Li Meng

**Publisher:** CRC Press

**ISBN:** 1420079425

**Category:** Mathematics

**Page:** 619

**View:** 1375

Computers

**Author**: Jean-Michel Marin,Christian P. Robert

**Publisher:** Springer Science & Business Media

**ISBN:** 1461486874

**Category:** Computers

**Page:** 296

**View:** 7746

Mathematics

*Analytic and Monte Carlo Computations*

**Author**: Carl Graham

**Publisher:** John Wiley & Sons

**ISBN:** 1118882695

**Category:** Mathematics

**Page:** 264

**View:** 4661

Mathematics

**Author**: Michael J. Crawley

**Publisher:** John Wiley & Sons

**ISBN:** 1118448960

**Category:** Mathematics

**Page:** 1080

**View:** 998

Mathematics

**Author**: Paulo Cortez

**Publisher:** Springer

**ISBN:** 3319082639

**Category:** Mathematics

**Page:** 188

**View:** 3467

Mathematics

**Author**: Owen Jones,Robert Maillardet,Andrew Robinson

**Publisher:** CRC Press

**ISBN:** 1466570016

**Category:** Mathematics

**Page:** 606

**View:** 4819

Mathematics

**Author**: Ioannis Ntzoufras

**Publisher:** John Wiley & Sons

**ISBN:** 1118210352

**Category:** Mathematics

**Page:** 520

**View:** 7616

Computers

*A Tour of Statistical Software Design*

**Author**: Norman Matloff

**Publisher:** No Starch Press

**ISBN:** 1593273843

**Category:** Computers

**Page:** 400

**View:** 8611