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Full Description
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.
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 Gómez-Rubio, Guido Mazzinari, Bayesian growth curve model for studying the intra-abdominal volume during pneumoperitoneum for laparoscopic surgery.
