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Titlebook: Bayesian Statistics, New Generations New Approaches; BAYSM 2022, Montréal Alejandra Avalos-Pacheco,Roberta De Vito,Florian M Conference pro

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發(fā)表于 2025-3-21 17:53:16 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱Bayesian Statistics, New Generations New Approaches
期刊簡(jiǎn)稱BAYSM 2022, Montréal
影響因子2023Alejandra Avalos-Pacheco,Roberta De Vito,Florian M
視頻videohttp://file.papertrans.cn/182/181886/181886.mp4
發(fā)行地址Provides recent advanced techniques in Bayesian analysis.Focus on contributions with novel Bayesian approaches that tackle a problem of key importance.Produces a wide range of approaches for Bayesian
學(xué)科分類Springer Proceedings in Mathematics & Statistics
圖書封面Titlebook: Bayesian Statistics, New Generations New Approaches; BAYSM 2022, Montréal Alejandra Avalos-Pacheco,Roberta De Vito,Florian M Conference pro
影響因子.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..
Pindex Conference proceedings 2023
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Caryn Hoang,James D. Miles,Kim-Phuong L. Vued by the mean posterior distributions, we can outperform existing methods in computational time whilst providing comparable model scores. This method also enables us to learn more complex relationships than existing model selection techniques by expanding the model space. We illustrate how this can embellish inferences in a real study.
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Zitao Cheng,Keiko Kasamatsu,Takeo Ainoya Bayesian Computation, a powerful simulation-based inference method, to provide posterior estimates of the model’s parameters. Using these approximate posterior distributions, we predicted the prevalence of current, former, and never smokers in Tuscany up to 2043. The model results suggest that the prevalence of smokers will decrease over time.
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Speeding up the Zig-Zag Process,eoretical results and we will present a numerical study on some more practical models than the ones discussed in Vasdekis G. and Roberts G. O. (2023+) [.], showing that the advantages of using SUZZ may also extend to lighter tailed targets.
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