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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.
List 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 Gómez-Rubio, Guido Mazzinari, Bayesian growth curve model for studying the intra-abdominal volume during pneumoperitoneum for laparoscopic surgery.
About the author
Raffaele Argiento is a Full Professor of Statistics at the Department of Economics of the University of Bergamo, Italy. He is member of Economics, Statistics and Data Science Ph.D board at the University of Milano-Bicocca and he is affiliated with the "de Castro" Statistics initiative of the Collegio Carlo Alberto, Turin. His research focuses on Bayesian finite and infinite mixture models with a particular focus on the associated computational strategies as well as the related model based clustering.
Federico Camerlenghi is an Assistant Professor of Statistics at the Department of Economics, Management and Statistics and a board member of the Ph.D. in Economics, Statistics and Data Science at the University of Milano-Bicocca, Italy. His research mainly focuses on the construction and investigation of Bayesian nonparameteric models to handle exchangeable and partially exchangeable data. He received the Savage award in Theory & Methods in 2017, and he was the chair of the junior Section of the International Society for Bayesian Analysis (j-ISBA) in 2019.
Sally Paganin is a Research fellow in the Department of Biostatistics at Harvard T.H. Chan School of Public Health, and treasurer of the junior Section of the International Society for Bayesian Analysis (j-ISBA). Previously, she was a Postdoctoral Researcher at UC Berkeley, and a core team member of the NIMBLE software project. Her research focuses on Bayesian methods and statistical models for complex data, along with the development of statistical software and algorithms.
