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Titlebook: New Frontiers in Bayesian Statistics; BAYSM 2021, Online, Raffaele Argiento,Federico Camerlenghi,Sally Pagan Conference proceedings 2022 T

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發(fā)表于 2025-3-21 19:36:19 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱(chēng)New Frontiers in Bayesian Statistics
副標(biāo)題BAYSM 2021, Online,
編輯Raffaele Argiento,Federico Camerlenghi,Sally Pagan
視頻videohttp://file.papertrans.cn/666/665259/665259.mp4
概述It reports the best work of young Bayesian statisticians.It provides a quite large overview of the current research in the field.It can be seen as a springboard for talented young Bayesian to write th
叢書(shū)名稱(chēng)Springer Proceedings in Mathematics & Statistics
圖書(shū)封面Titlebook: New Frontiers in Bayesian Statistics; BAYSM 2021, Online,  Raffaele Argiento,Federico Camerlenghi,Sally Pagan Conference proceedings 2022 T
描述.This book presents a selection of peer-reviewed contributions to the fifth Bayesian Young Statisticians Meeting, BaYSM 2021, held virtually due to the COVID-19 pandemic on 1-3 September 2021. Despite all the challenges of an online conference, the meeting provided a valuable opportunity for early career researchers, including MSc students, PhD students, and postdocs to connect with the broader Bayesian community...The proceedings highlight many different topics in Bayesian statistics, presenting promising methodological approaches to address important challenges in a variety of applications. The book is intended for a broad audience of people interested in statistics, and provides a series of stimulating contributions on theoretical, methodological, and computational aspects of Bayesian statistics..
出版日期Conference proceedings 2022
關(guān)鍵詞Bayesian Statistics; Mixture models; Markov Chain; Monte Carlo algorithms; Bayesian Nonparametrics; Survi
版次1
doihttps://doi.org/10.1007/978-3-031-16427-9
isbn_softcover978-3-031-16429-3
isbn_ebook978-3-031-16427-9Series ISSN 2194-1009 Series E-ISSN 2194-1017
issn_series 2194-1009
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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https://doi.org/10.1007/978-3-031-16427-9Bayesian Statistics; Mixture models; Markov Chain; Monte Carlo algorithms; Bayesian Nonparametrics; Survi
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978-3-031-16429-3The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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in from the start, that is, we wish to gauge the reparameterization group. In this way, one should be able to select as gauge choices either the endpoint or midpoint as special points along the string. Then, we should be able to view the various string field theories as gauge choices, that is, the
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Yuanqi Chu,Xueping Hu,Keming Yu in from the start, that is, we wish to gauge the reparameterization group. In this way, one should be able to select as gauge choices either the endpoint or midpoint as special points along the string. Then, we should be able to view the various string field theories as gauge choices, that is, the
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