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
Table of Contents:
  • 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.