| 期刊全稱 | Bayesian Computation with R | | 影響因子2023 | Jim Albert | | 視頻video | http://file.papertrans.cn/182/181833/181833.mp4 | | 發(fā)行地址 | Introduces Bayesian modeling by use of computation using the R language.Includes supplementary material: | | 學(xué)科分類 | Use R! | | 圖書(shū)封面 |  | | 影響因子 | 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. Moreov | | Pindex | Textbook 20071st edition |
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