New frontiers in Bayesian statistics : BAYSM 2021, online, September 1-3 / Raffaele Argiento, Federico Camerlenghi, Sally Paganin, editors.

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 ca...

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Bibliographic Details
Corporate Author: BAYSM Online
Other Authors: Argiento, Raffaele (Editor), Camerlenghi, Federico (Editor), Paganin, Sally (Editor)
Format: eBook
Language:English
Published: Cham : Springer, [2022]
Series:Springer proceedings in mathematics & statistics ; v.405.
Subjects:
Online Access:Click for online access

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245 1 0 |a New frontiers in Bayesian statistics :  |b BAYSM 2021, online, September 1-3 /  |c Raffaele Argiento, Federico Camerlenghi, Sally Paganin, editors. 
246 3 0 |a BAYSM 2021 
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490 1 |a Springer proceedings in mathematics & statistics ;  |v volume 405 
500 |a Selected conference proceedings. 
500 |a Includes author index. 
520 |a 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. 
505 0 |a 1 Andrej Srakar, Approximate Bayesian algorithm for tensor robust principal component analysis -- 2 Yuanqi Chu, Xueping Hu, Keming Yu, Bayesian Quantile Regression for Big Data Analysis -- 3 Peter Strong, Alys McAlphine, Jim Smith, Towards A Bayesian Analysis of Migration Pathways using Chain Event Graphs of Agent Based Models -- 4 Giorgos Tzoumerkas, Dimitris Fouskakis, Power-Expected-Posterior Methodology with Baseline Shrinkage Priors -- 5 Mica Teo, Sara Wade, Bayesian nonparametric scalar-on-image regression via Potts-Gibbs random partition models -- 6 Alessandro Colombi, Block Structured Graph Priors in Gaussian Graphical Models -- 7 Jessica Pavani, Paula Moraga, A Bayesian joint spatio-temporal model for multiple mosquito-borne diseases -- 8 Ivan Gutierrez, Luis Gutierrez, Danilo Alvare, A Bayesian nonparametric test for cross-group differences relative to a control -- 9 Francesco Gaffi, Antonio Lijoi, Igor Pruenster, Specification of the base measure of nonparametric priors via random means -- 10 Matteo Pedone, Raffaele Argiento, Francesco Claudio Stingo, Bayesian Nonparametric Predictive Modeling for Personalized Treatment Selection -- 11 Gabriel Calvo, carmen armero, Virgilio Gomez-Rubio, Guido Mazzinari, Bayesian growth curve model for studying the intra-abdominal volume during pneumoperitoneum for laparoscopic surgery. 
588 0 |a Print version record. 
650 0 |a Bayesian statistical decision theory  |v Congresses. 
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655 7 |a Conference papers and proceedings.  |2 lcgft 
655 7 |a proceedings (reports)  |2 aat 
655 7 |a Conference papers and proceedings  |2 fast 
655 7 |a Actes de congrès.  |2 rvmgf 
700 1 |a Argiento, Raffaele,  |e editor. 
700 1 |a Camerlenghi, Federico,  |e editor. 
700 1 |a Paganin, Sally,  |e editor. 
776 0 8 |i Print version:  |t NEW FRONTIERS IN BAYESIAN STATISTICS.  |d [Place of publication not identified] : SPRINGER INTERNATIONAL PU, 2022  |z 3031164261  |w (OCoLC)1338830821 
830 0 |a Springer proceedings in mathematics & statistics ;  |v v.405. 
856 4 0 |u https://holycross.idm.oclc.org/login?auth=cas&url=https://link.springer.com/10.1007/978-3-031-16427-9  |y Click for online access 
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