Monte Carlo Statistical Methods

Author: Christian Robert
Publisher: Springer Science & Business Media
ISBN: 9781475741452
Release Date: 2013-03-14
Genre: Mathematics

We have sold 4300 copies worldwide of the first edition (1999). This new edition contains five completely new chapters covering new developments.

Monte Carlo Statistical Methods

Author: Christian Robert
Publisher: Springer Science & Business Media
ISBN: 9781475730715
Release Date: 2013-03-14
Genre: Mathematics

We have sold 4300 copies worldwide of the first edition (1999). This new edition contains five completely new chapters covering new developments.

Monte Carlo Statistical Methods

Author: Christian Robert
Publisher: Springer
ISBN: 9780387212395
Release Date: 2005-08-24
Genre: Mathematics

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
Publisher: Springer Science & Business Media
ISBN: 0387924078
Release Date: 2009-06-02
Genre: Mathematics

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.

The Bayesian Choice

Author: Christian Robert
Publisher: Springer Science & Business Media
ISBN: 9780387715995
Release Date: 2007-05-19
Genre: Mathematics

This is an introduction to Bayesian statistics and decision theory, including advanced topics such as Monte Carlo methods. This new edition contains several revised chapters and a new chapter on model choice.

Introducing Monte Carlo Methods with R

Author: Christian Robert
Publisher: Springer Science & Business Media
ISBN: 9781441915757
Release Date: 2009-12-10
Genre: Computers

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.

Monte Carlo Methods in Bayesian Computation

Author: Ming-Hui Chen
Publisher: Springer Science & Business Media
ISBN: 9781461212768
Release Date: 2012-12-06
Genre: Mathematics

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.

Explorations in Monte Carlo Methods

Author: Ronald W. Shonkwiler
Publisher: Springer Science & Business Media
ISBN: 9780387878379
Release Date: 2009-08-11
Genre: Mathematics

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 Strategies in Scientific Computing

Author: Jun S. Liu
Publisher: Springer Science & Business Media
ISBN: 9780387763712
Release Date: 2013-11-11
Genre: Mathematics

This book provides a self-contained and up-to-date treatment of the Monte Carlo method and develops a common framework under which various Monte Carlo techniques can be "standardized" and compared. Given the interdisciplinary nature of the topics and a moderate prerequisite for the reader, this book should be of interest to a broad audience of quantitative researchers such as computational biologists, computer scientists, econometricians, engineers, probabilists, and statisticians. It can also be used as a textbook for a graduate-level course on Monte Carlo methods.

Random Number Generation and Monte Carlo Methods

Author: James E. Gentle
Publisher: Springer Science & Business Media
ISBN: 9781475729603
Release Date: 2013-03-14
Genre: Computers

Monte Carlo simulation has become one of the most important tools in all fields of science. This book surveys the basic techniques and principles of the subject, as well as general techniques useful in more complicated models and in novel settings. The emphasis throughout is on practical methods that work well in current computing environments.

Essentials of Monte Carlo Simulation

Author: Nick T. Thomopoulos
Publisher: Springer Science & Business Media
ISBN: 9781461460220
Release Date: 2012-12-19
Genre: Mathematics

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.

Bayesian Core A Practical Approach to Computational Bayesian Statistics

Author: Jean-Michel Marin
Publisher: Springer Science & Business Media
ISBN: 9780387389790
Release Date: 2007-02-06
Genre: Computers

This Bayesian modeling book is intended for practitioners and applied statisticians looking for a self-contained entry to computational Bayesian statistics. Focusing on standard statistical models, it provides an operational methodology for conducting Bayesian inference, rather than focusing on its theoretical justifications.

Markov Chain Monte Carlo

Author: Dani Gamerman
Publisher: CRC Press
ISBN: 9781482296426
Release Date: 2006-05-10
Genre: Mathematics

While there have been few theoretical contributions on the Markov Chain Monte Carlo (MCMC) methods in the past decade, current understanding and application of MCMC to the solution of inference problems has increased by leaps and bounds. Incorporating changes in theory and highlighting new applications, Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference, Second Edition presents a concise, accessible, and comprehensive introduction to the methods of this valuable simulation technique. The second edition includes access to an internet site that provides the code, written in R and WinBUGS, used in many of the previously existing and new examples and exercises. More importantly, the self-explanatory nature of the codes will enable modification of the inputs to the codes and variation on many directions will be available for further exploration. Major changes from the previous edition: · More examples with discussion of computational details in chapters on Gibbs sampling and Metropolis-Hastings algorithms · Recent developments in MCMC, including reversible jump, slice sampling, bridge sampling, path sampling, multiple-try, and delayed rejection · Discussion of computation using both R and WinBUGS · Additional exercises and selected solutions within the text, with all data sets and software available for download from the Web · Sections on spatial models and model adequacy The self-contained text units make MCMC accessible to scientists in other disciplines as well as statisticians. The book will appeal to everyone working with MCMC techniques, especially research and graduate statisticians and biostatisticians, and scientists handling data and formulating models. The book has been substantially reinforced as a first reading of material on MCMC and, consequently, as a textbook for modern Bayesian computation and Bayesian inference courses.

Essential Statistical Inference

Author: Dennis D. Boos
Publisher: Springer Science & Business Media
ISBN: 9781461448181
Release Date: 2013-02-06
Genre: Mathematics

​This book is for students and researchers who have had a first year graduate level mathematical statistics course. It covers classical likelihood, Bayesian, and permutation inference; an introduction to basic asymptotic distribution theory; and modern topics like M-estimation, the jackknife, and the bootstrap. R code is woven throughout the text, and there are a large number of examples and problems. An important goal has been to make the topics accessible to a wide audience, with little overt reliance on measure theory. A typical semester course consists of Chapters 1-6 (likelihood-based estimation and testing, Bayesian inference, basic asymptotic results) plus selections from M-estimation and related testing and resampling methodology. Dennis Boos and Len Stefanski are professors in the Department of Statistics at North Carolina State. Their research has been eclectic, often with a robustness angle, although Stefanski is also known for research concentrated on measurement error, including a co-authored book on non-linear measurement error models. In recent years the authors have jointly worked on variable selection methods. ​

Exploring Monte Carlo Methods

Author: William L. Dunn
Publisher: Elsevier
ISBN: 0080930611
Release Date: 2011-05-24
Genre: Mathematics

Exploring Monte Carlo Methods is a basic text that describes the numerical methods that have come to be known as "Monte Carlo." The book treats the subject generically through the first eight chapters and, thus, should be of use to anyone who wants to learn to use Monte Carlo. The next two chapters focus on applications in nuclear engineering, which are illustrative of uses in other fields. Five appendices are included, which provide useful information on probability distributions, general-purpose Monte Carlo codes for radiation transport, and other matters. The famous "Buffon’s needle problem" provides a unifying theme as it is repeatedly used to illustrate many features of Monte Carlo methods. This book provides the basic detail necessary to learn how to apply Monte Carlo methods and thus should be useful as a text book for undergraduate or graduate courses in numerical methods. It is written so that interested readers with only an understanding of calculus and differential equations can learn Monte Carlo on their own. Coverage of topics such as variance reduction, pseudo-random number generation, Markov chain Monte Carlo, inverse Monte Carlo, and linear operator equations will make the book useful even to experienced Monte Carlo practitioners. Provides a concise treatment of generic Monte Carlo methods Proofs for each chapter Appendixes include Certain mathematical functions; Bose Einstein functions, Fermi Dirac functions, Watson functions