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Titlebook: Bayesian Core: A Practical Approach to Computational Bayesian Statistics; Jean-Michel Marin,Christian P. Robert Textbook 20071st edition S

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期刊全称Bayesian Core: A Practical Approach to Computational Bayesian Statistics
影响因子2023Jean-Michel Marin,Christian P. Robert
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发行地址The perfect entry for gaining a practical understanding of Bayesian methodology.Guides the reader into the practice of prior modeling and Bayesian computing for the most classical models.Computational
学科分类Springer Texts in Statistics
图书封面Titlebook: Bayesian Core: A Practical Approach to Computational Bayesian Statistics;  Jean-Michel Marin,Christian P. Robert Textbook 20071st edition S
影响因子After that, it was down to attitude. —Ian Rankin, Black & Blue. — The purpose of this book is to provide a self-contained (we insist!) entry into practical and computational Bayesian statistics using generic examples from the most common models for a class duration of about seven blocks that roughly correspond to 13 to 15 weeks of teaching (with three hours of lectures per week), depending on the intended level and the prerequisites imposed on the students. (That estimate does not include practice—i. e. , programming labs—since those may have a variable duration, also depending on the s- dents’ involvement and their programming abilities. ) The emphasis on practice is a strong feature of this book in that its primary audience consists of gr- uate students who need to use (Bayesian) statistics as a tool to analyze their experiments and/or datasets. The book should also appeal to scientists in all ?elds, given the versatility of the Bayesian tools. It can also be used for a more classical statistics audience when aimed at teaching a quick entry to Bayesian statistics at the end of an undergraduate program for instance. (Obviously, it can supplement another textbook on data analysis a
Pindex Textbook 20071st edition
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Springer-Verlag New York 2007
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Human Capital and Economic Growth testing assimilation of the techniques. We then propose a corresponding statistical model centered on the normal N (µ, σ.) distribution and consider specific inferential questions to address at this level, namely parameter estimation, one-sided test, prediction, and outlier detection, after we set
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Giam Pietro Cipriani,Tamara Fioroniand thus to uncover explanatory and predictive patterns. This chapter unfolds the Bayesian analysis of the linear model both in terms of prior specification (conjugate, noninformative, and Zellner’s G-prior) and in terms of variable selection, the next chapter appearing as a sequel for nonlinear dep
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https://doi.org/10.1057/978-1-137-56561-7a single transformation of the data that must achieve the possibly conflicting goals of normality and linearity imposed by the linear regression model, which is for instance impossible for binary or count responses. The trick that allows both a feasible processing and an extension of linear regressi
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Occupational Characteristics Analysisth series in the picture above!). As in the previous chapters, the difficulty in modeling such datasets is to balance the complexity of the representation of the dependence structure against the estimation of the corresponding model—and thus the modeling most often involves model choice or model com
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