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Titlebook: Bayesian Statistics from Methods to Models and Applications; Research from BAYSM Sylvia Frühwirth-Schnatter,Angela Bitto,Alexandra Confer

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期刊全称Bayesian Statistics from Methods to Models and Applications
期刊简称Research from BAYSM
影响因子2023Sylvia Frühwirth-Schnatter,Angela Bitto,Alexandra
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发行地址Original contributions from BAYSM 2014 researchers cover recent developments in Bayesian statistics.Includes academic as well as industrial research and applications of Bayesian statistics.Incorporate
学科分类Springer Proceedings in Mathematics & Statistics
图书封面Titlebook: Bayesian Statistics from Methods to Models and Applications; Research from BAYSM  Sylvia Frühwirth-Schnatter,Angela Bitto,Alexandra  Confer
影响因子.The Second Bayesian Young Statisticians Meeting (BAYSM 2014) and the research presented here facilitate connections among researchers using Bayesian Statistics by providing a forum for the development and exchange of ideas. WU Vienna University of Business and Economics hosted BAYSM 2014 from September 18.th. to the 19.th.. The guidance of renowned plenary lecturers and senior discussants is a critical part of the meeting and this volume, which follows publication of contributions from BAYSM 2013. The meeting‘s scientific program reflected the variety of fields in which Bayesian methods are currently employed or could be introduced in the future. Three brilliant keynote lectures by Chris Holmes (University of Oxford), Christian Robert (Université Paris-Dauphine), and Mike West (Duke University), were complemented by 24 plenary talks covering the major topics Dynamic Models, Applications, Bayesian Nonparametrics, Biostatistics, Bayesian Methods in Economics, and Models and Methods, as well as a lively poster session with 30 contributions. Selected contributions have been drawn from the conference for this book. All contributions in this volume are peer-reviewed and share original r
Pindex Conference proceedings 2015
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A Subordinated Stochastic Process Modelare irregularly spaced in time or different curves that are observed at different moments of time. With the use of simulations and applications, we examine two approaches to Bayesian inference for our model: the first based on a Gibbs sampler and the second based on approximate Bayesian computation (ABC).
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