
Introducing Monte Carlo Methods with R by Christian Robert
Basic R Programming.- Random Variable Generation.- Monte Carlo Integration.- Controlling and Accelerating Convergence.- Monte Carlo Optimization.- Metropolis#x2013;Hastings Algorithms.- Gibbs Samplers.- Convergence Monitoring and Adaptation for MCMC Algorithms.-
A Primer of Ecology with R
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Introductory Time Series with R
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ggplot2
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Bayesian Networks in R
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A Users Guide to Network Analysis in R
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Bayesian Computation with R
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Biostatistics with R
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R For Marketing Research and Analytics
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Data Manipulation with R
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Applied Spatial Data Analysis with R
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R Coding for Ecology
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Bayesian Cost-Effectiveness Analysis with the R package BCEA
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Geostatistics for Compositional Data with R
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Modeling Dose-Response Microarray Data in Early Drug Development Experiments Using R
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Behavioral Research Data Analysis with R
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Discrete Choice Analysis with R
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R by Example
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Semiparametric Regression with R
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Heart Rate Variability Analysis with the R package RHRV
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Modeling Psychophysical Data in R
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Elements of Copula Modeling with R
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Modern Optimization with R
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Cultural Analytics in R: A Tidy Approach
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An Introduction to Web Mining
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Numerical Ecology with R
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Morphometrics with R
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Analysis of Integrated and Cointegrated Time Series with R
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Multistate Analysis of Life Histories with R
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Computerized Adaptive and Multistage Testing with R
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Analyzing Compositional Data with R
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Solving Differential Equations in R
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Functional and Phylogenetic Ecology in R
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Functional Data Analysis with R and MATLAB
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Mixture and Hidden Markov Models with R
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Singular Spectrum Analysis with R
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Retirement Income Recipes in R
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Quality Control with R
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Analysis of Phylogenetics and Evolution with R
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Dynamic Linear Models with R
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Applied Survival Analysis Using R
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Magnetic Resonance Brain Imaging
From the reviews:
“Robert and Casella’s new book uses the programming language R, a favorite amongst (Bayesian) statisticians to introduce in eight chapters both basic and advanced Monte Carlo techniques …The book could be used as the basic textbook for a semester long course on computational statistics with emphasis on Monte Carlo tools … . useful for (and should be next to the computer of) a large body of hands on graduate students, researchers, instructors and practitioners … .” (Hedibert Freitas Lopes, Journal of the American Statistical Association, Vol. 106 (493), March, 2011)
“Chapters focuses on MCMC methods the Metropolis–Hastings algorithm, Gibbs sampling, and monitoring and adaptation for MCMC algorithms. … There are exercises within and at the end of all chapters … . Overall, the level of the book makes it suitable for graduate students and researchers. Others who wish to implement Monte Carlo methods, particularly MCMC methods for Bayesian analysis will also find it useful.” (David Scott, International Statistical Review, Vol. 78 (3), 2010)
“The primary audience is graduate students in statistics, biostatistics, engineering, etc. who need to know how to utilize Monte Carlo simulation methods to analyze their experiments and/or datasets. … this text does an effective job of including a selection of Monte Carlo methods and their application to a broad array of simulation problems. … Anyone who is an avid R user and has need to integrate and/or optimize complex functions will find this text to be a necessary addition to his or her personal library.” (Dean V. Neubauer, Technometrics, Vol. 53 (2), May, 2011)
| SKU | Unavailable |
| ISBN 13 | 9781441915757 |
| ISBN 10 | 1441915753 |
| Title | Introducing Monte Carlo Methods with R |
| Author | Christian Robert |
| Series | Use R! |
| Condition | Unavailable |
| Binding Type | Paperback |
| Publisher | Springer-Verlag New York Inc. |
| Year published | 2009-12-10 |
| Number of pages | 284 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |








































