Bayesian Statistics, New Generations New Approaches
BAYSM 2022, Montréal, Canada, June 22–23
Series: Springer Proceedings in Mathematics & Statistics; 435;
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Product details:
- Edition number 1st ed. 2023
- Publisher Springer International Publishing
- Date of Publication 30 November 2023
- Number of Volumes 1 pieces, Book
- ISBN 9783031424120
- Binding Hardback
- No. of pages115 pages
- Size 235x155 mm
- Weight 382 g
- Language English
- Illustrations VIII, 115 p. 34 illus., 22 illus. in color. Illustrations, black & white 516
Categories
Long description:
"
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 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.
" MoreTable of Contents:
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.
