Introducing Monte Carlo Methods With R PDF Download
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Author | : Christian Robert |
Publisher | : Springer Science & Business Media |
Total Pages | : 297 |
Release | : 2010 |
Genre | : Computers |
ISBN | : 1441915753 |
Download Introducing Monte Carlo Methods with R Book in PDF, ePub and Kindle
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.
Author | : Christian Robert |
Publisher | : Springer Science & Business Media |
Total Pages | : 297 |
Release | : 2009-11-24 |
Genre | : Mathematics |
ISBN | : 1441915761 |
Download Introducing Monte Carlo Methods with R Book in PDF, ePub and Kindle
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.
Author | : Christian P. Robert |
Publisher | : |
Total Pages | : 306 |
Release | : 2010-04-17 |
Genre | : |
ISBN | : 9781441915825 |
Download Introducing Monte Carlo Methods with R Book in PDF, ePub and Kindle
Author | : Christian Robert |
Publisher | : Springer Science & Business Media |
Total Pages | : 670 |
Release | : 2013-03-14 |
Genre | : Mathematics |
ISBN | : 1475741456 |
Download Monte Carlo Statistical Methods Book in PDF, ePub and Kindle
We have sold 4300 copies worldwide of the first edition (1999). This new edition contains five completely new chapters covering new developments.
Author | : Steve Brooks |
Publisher | : CRC Press |
Total Pages | : 620 |
Release | : 2011-05-10 |
Genre | : Mathematics |
ISBN | : 1420079425 |
Download Handbook of Markov Chain Monte Carlo Book in PDF, ePub and Kindle
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 fisherie
Author | : Eric A. Suess |
Publisher | : Springer Science & Business Media |
Total Pages | : 317 |
Release | : 2010-06-15 |
Genre | : Mathematics |
ISBN | : 038740273X |
Download Introduction to Probability Simulation and Gibbs Sampling with R Book in PDF, ePub and Kindle
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.
Author | : Adrian Barbu |
Publisher | : Springer Nature |
Total Pages | : 433 |
Release | : 2020-02-24 |
Genre | : Mathematics |
ISBN | : 9811329710 |
Download Monte Carlo Methods Book in PDF, ePub and Kindle
This book seeks to bridge the gap between statistics and computer science. It provides an overview of Monte Carlo methods, including Sequential Monte Carlo, Markov Chain Monte Carlo, Metropolis-Hastings, Gibbs Sampler, Cluster Sampling, Data Driven MCMC, Stochastic Gradient descent, Langevin Monte Carlo, Hamiltonian Monte Carlo, and energy landscape mapping. Due to its comprehensive nature, the book is suitable for developing and teaching graduate courses on Monte Carlo methods. To facilitate learning, each chapter includes several representative application examples from various fields. The book pursues two main goals: (1) It introduces researchers to applying Monte Carlo methods to broader problems in areas such as Computer Vision, Computer Graphics, Machine Learning, Robotics, Artificial Intelligence, etc.; and (2) it makes it easier for scientists and engineers working in these areas to employ Monte Carlo methods to enhance their research.
Author | : Thomas M. Carsey |
Publisher | : SAGE Publications |
Total Pages | : 304 |
Release | : 2013-08-05 |
Genre | : Social Science |
ISBN | : 1483324923 |
Download Monte Carlo Simulation and Resampling Methods for Social Science Book in PDF, ePub and Kindle
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.
Author | : Dani Gamerman |
Publisher | : CRC Press |
Total Pages | : 264 |
Release | : 1997-10-01 |
Genre | : Mathematics |
ISBN | : 9780412818202 |
Download Markov Chain Monte Carlo Book in PDF, ePub and Kindle
Bridging the gap between research and application, Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference provides a concise, and integrated account of Markov chain Monte Carlo (MCMC) for performing Bayesian inference. This volume, which was developed from a short course taught by the author at a meeting of Brazilian statisticians and probabilists, retains the didactic character of the original course text. The self-contained text units make MCMC accessible to scientists in other disciplines as well as statisticians. It describes each component of the theory in detail and outlines related software, which is of particular benefit to applied scientists.
Author | : Arnaud Doucet |
Publisher | : Springer Science & Business Media |
Total Pages | : 590 |
Release | : 2013-03-09 |
Genre | : Mathematics |
ISBN | : 1475734379 |
Download Sequential Monte Carlo Methods in Practice Book in PDF, ePub and Kindle
Monte Carlo methods are revolutionizing the on-line analysis of data in many fileds. They have made it possible to solve numerically many complex, non-standard problems that were previously intractable. This book presents the first comprehensive treatment of these techniques.