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＜電子ブック＞
Case Studies in Applied Bayesian Data Science : CIRM Jean-Morlet Chair, Fall 2018 / edited by Kerrie L. Mengersen, Pierre Pudlo, Christian P. Robert
(Lecture Notes in Mathematics. ISSN:16179692 ; 2259)

版	1st ed. 2020.
出版者	Cham : Springer International Publishing : Imprint: Springer
出版年	2020
本文言語	英語
大きさ	VI, 420 p. 110 illus., 94 illus. in color : online resource
著者標目	Mengersen, Kerrie L editor Pudlo, Pierre editor Robert, Christian P editor SpringerLink (Online service)
件　名	LCSH:Statistics  LCSH:Probabilities FREE:Bayesian Inference FREE:Probability Theory FREE:Applied Statistics
一般注記	Presenting a range of substantive applied problems within Bayesian Statistics along with their Bayesian solutions, this book arises from a research program at CIRM in France in the second semester of 2018, which supported Kerrie Mengersen as a visiting Jean-Morlet Chair and Pierre Pudlo as the local Research Professor. The field of Bayesian statistics has exploded over the past thirty years and is now an established field of research in mathematical statistics and computer science, a key component of data science, and an underpinning methodology in many domains of science, business and social science. Moreover, while remaining naturally entwined, the three arms of Bayesian statistics, namely modelling, computation and inference, have grown into independent research fields.While the research arms of Bayesian statistics continue to grow in many directions, they are harnessed when attention turns to solving substantive applied problems. Each such problem set has its own challenges and hence draws from the suite of research a bespoke solution. The book will be useful for both theoretical and applied statisticians, as well as practitioners, to inspect these solutions in the context of the problems, in order to draw further understanding, awareness and inspiration. HTTP:URL=https://doi.org/10.1007/978-3-030-42553-1

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