Bayesian Computation with R by Jim Albert
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).-
A Primer of Ecology with R
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Introductory Time Series with R
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Bayesian Networks in R
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ggplot2
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A Users Guide to Network Analysis in 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
| SKU | Unavailable |
| ISBN 13 | 9780387922973 |
| ISBN 10 | 0387922970 |
| Title | Bayesian Computation with R |
| Author | Jim Albert |
| Series | Use R! |
| Condition | Unavailable |
| Binding Type | Paperback |
| Publisher | Springer-Verlag New York Inc. |
| Year published | 2009-05-15 |
| Number of pages | 300 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |