| 书目名称 | Robust Bayesian Analysis | | 编辑 | David Ríos Insua,Fabrizio Ruggeri | | 视频video | | | 丛书名称 | Lecture Notes in Statistics | | 图书封面 |  | | 描述 | Robust Bayesian analysis aims at overcoming the traditional objection to Bayesian analysis of its dependence on subjective inputs, mainly the prior and the loss. Its purpose is the determination of the impact of the inputs to a Bayesian analysis (the prior, the loss and the model) on its output when the inputs range in certain classes. If the impact is considerable, there is sensitivity and we should attempt to further refine the information the incumbent classes available, perhaps through additional constraints on and/ or obtaining additional data; if the impact is not important, robustness holds and no further analysis and refinement would be required. Robust Bayesian analysis has been widely accepted by Bayesian statisticians; for a while it was even a main research topic in the field. However, to a great extent, their impact is yet to be seen in applied settings. This volume, therefore, presents an overview of the current state of robust Bayesian methods and their applications and identifies topics of further in terest in the area. The papers in the volume are divided into nine parts covering the main aspects of the field. The first one provides an overview of Bayesian robustn | | 出版日期 | Book 2000 | | 关键词 | Markov chain Monte Carlo; Scheme; Volume; computation; decision problem; likelihood; maintenance; metrics; m | | 版次 | 1 | | doi | https://doi.org/10.1007/978-1-4612-1306-2 | | isbn_softcover | 978-0-387-98866-5 | | isbn_ebook | 978-1-4612-1306-2Series ISSN 0930-0325 Series E-ISSN 2197-7186 | | issn_series | 0930-0325 | | copyright | Springer Science+Business Media New York 2000 |
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发表于 2025-3-21 16:53:55
