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Titlebook: Bayesian Essentials with R; Jean-Michel Marin,Christian P. Robert Textbook 2014Latest edition Springer Science+Business Media, LLC, part o

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發(fā)表于 2025-3-21 19:31:08 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱Bayesian Essentials with R
影響因子2023Jean-Michel Marin,Christian P. Robert
視頻videohttp://file.papertrans.cn/182/181838/181838.mp4
發(fā)行地址New Complete Solutions Manual for readers available on Springer book page.No prior knowledge of R required to learn the essentials for using it with Bayesian statistics.Each chapter includes exercises
學(xué)科分類Springer Texts in Statistics
圖書封面Titlebook: Bayesian Essentials with R;  Jean-Michel Marin,Christian P. Robert Textbook 2014Latest edition Springer Science+Business Media, LLC, part o
影響因子.This Bayesian modeling book provides a self-contained entry to computational Bayesian statistics. Focusing on the most standard statistical models and backed up by real datasets and an all-inclusive R (CRAN) package called bayess, the book provides an operational methodology for conducting Bayesian inference, rather than focusing on its theoretical and philosophical justifications.?.Readers are empowered to participate in the real-life data analysis situations depicted here from the beginning. Special attention is paid to the derivation of prior distributions in each case and specific reference solutions are given for each of the models. Similarly, computational details are worked out to lead the reader towards an effective programming of the methods given in the book. In particular, all R codes are discussed with enough detail to make them readily understandable and expandable.?.Bayesian Essentials with R .can be used as a textbook at both undergraduate and graduate levels. It is particularly useful with students in professional degree programs and scientists to analyze data the Bayesian way. The text will also enhance introductory courses on Bayesian statistics. Prerequisites fo
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Textbook 2014Latest editionan Essentials with R .can be used as a textbook at both undergraduate and graduate levels. It is particularly useful with students in professional degree programs and scientists to analyze data the Bayesian way. The text will also enhance introductory courses on Bayesian statistics. Prerequisites fo
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Image Analysis, chapter. This is also the perfect opportunity to cover the ABC method, as these models do not allow for a closed form likelihood. Image analysis has been a very active area for both Bayesian statistics and computational methods in the past 30 years, so we feel it well deserves a chapter of its own for its specific features.
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Textbook 2014Latest editiond backed up by real datasets and an all-inclusive R (CRAN) package called bayess, the book provides an operational methodology for conducting Bayesian inference, rather than focusing on its theoretical and philosophical justifications.?.Readers are empowered to participate in the real-life data anal
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978-1-4939-5049-2Springer Science+Business Media, LLC, part of Springer Nature 2014
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Jean-Michel Marin,Christian P. RobertNew Complete Solutions Manual for readers available on Springer book page.No prior knowledge of R required to learn the essentials for using it with Bayesian statistics.Each chapter includes exercises
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Springer Texts in Statisticshttp://image.papertrans.cn/b/image/181838.jpg
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