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1. |
Record Nr. |
UNINA9910768195003321 |
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Autore |
Avalos-Pacheco Alejandra |
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Titolo |
Bayesian Statistics, New Generations New Approaches : BAYSM 2022, Montréal, Canada, June 22–23 / / edited by Alejandra Avalos-Pacheco, Roberta De Vito, Florian Maire |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023 |
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ISBN |
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Edizione |
[1st ed. 2023.] |
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Descrizione fisica |
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1 online resource (119 pages) |
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Collana |
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Springer Proceedings in Mathematics & Statistics, , 2194-1017 ; ; 435 |
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Altri autori (Persone) |
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De VitoRoberta |
MaireFlorian |
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Disciplina |
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Soggetti |
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Statistics |
Statistical Theory and Methods |
Bayesian Network |
Bayesian Inference |
Estadística bayesiana |
Congressos |
Llibres electrònics |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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J. Owen, I. Vernon, J. Carter, Bayesian Emulation of Complex Computer Models with Structured Partial Discontinuities -- B. Hansen, A. Avalos-Pacheco, M. Russo, Roberta De Vito, A Variational Bayes Approach to Factor Analysis. P. Strong, Jim Q. Smith, Scalable Model Selection for Staged Trees: Mean-posterior Clustering and Binary Trees -- G. Vasdekis, Gareth O. Roberts, Speeding up the Zig-Zag process -- V. Ghidini, S. Legramanti, R. Argiento, Extended Stochastic Block Model with Spatial Covariates for Weighted Brain Networks -- A. Lachi, C. Viscardi, M. Baccini, Approximate Bayesian inference for smoking habit dynamics in Tuscany. |
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Sommario/riassunto |
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This book hosts the results presented at the 6th Bayesian Young Statisticians Meeting 2022 in Montréal, Canada, held on June 22–23, titled "Bayesian Statistics, New Generations New Approaches". This |
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collection features selected peer-reviewed contributions that showcase the vibrant and diverse research presented at meeting. This book is intended for a broad audience interested in statistics and aims at providing stimulating contributions to theoretical, methodological, and computational aspects of Bayesian statistics. The contributions highlight various topics in Bayesian statistics, presenting promising methodological approaches to address critical challenges across diverse applications. This compilation stands as a testament to the talent and potential within the j-ISBA community. This book is meant to serve as a catalyst for continued advancements in Bayesian methodology and its applications and encourages fruitful collaborations that push the boundaries ofstatistical research. |
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