| 期刊全稱(chēng) | Bayesian Core: A Practical Approach to Computational Bayesian Statistics | | 影響因子2023 | Jean-Michel Marin,Christian P. Robert | | 視頻video | http://file.papertrans.cn/182/181834/181834.mp4 | | 發(fā)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 | | 學(xué)科分類(lèi) | Springer Texts in Statistics | | 圖書(shū)封面 |  | | 影響因子 | 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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