Bayesian Statistics, New Generations New Approaches

This book hosts the results presented at the 6th Bayesian Young Statisticians Meeting 2022 in Montréal, Canada, held on June 2223, 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.

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 computation methods

Autorentext
Alejandra Avalos-Pacheco is an Universitätsassistent (Assistant Professor non-tenure track) in the Research Unit of Applied Statistics (ASTAT) at the Vienna University of Technology (TU Wien) and an affiliated member of the Harvard-MIT Center for Regulatory Science (CRS). Previously, she was a research fellow in statistics at the University of Florence. Prior to that, she was a postdoctoral fellow in Statistics at the CRS, Harvard University, and part of the Dana-Farber Cancer Institute. She holds a Ph.D. in Statistics from OxWASP, a joint program between the University of Warwick and Oxford. Her Ph.D. thesis was granted the 2019 Savage award in Applied Methodology. Her main research interests include high-dimensional inference, data integration and applied Bayesian statistical modelling. She is the current j-ISBA chair and a member of the BAYSM board.

Roberta De Vito is an assistant Professor in the department of Biostatistics and at the Data Science Initiative at Brown University. She completed her Ph.D. in Statistical Science at the University of Padua, advised by Giovanni Parmigiani at Harvard University and the Dana Farber Cancer Institute, where she developed her thesis work. Then, she was a postdoc at Princeton University in Barbara Engelhardt's group where she developed Bayesian and latent variable discrete model in high-dimensional biological and epidemiological data. Her main research interest is latent variable model, Bayesian non parametric, variable selection via sparsity prior, machine learning and big data with particular focus on genomics and epidemiology.

Florian Maire is an Assistant Professor at the Department of Mathematics and Statistics of Université de Montréal. He was a Postdoctoral Fellow at Insight SFI Research Centre for Data Analytics, University College Dublin. He holds a Ph.D. in Applied Mathematics from Telecom SudParis, Institut Mines-Telecom and Université Paris Cité (ex Université Paris 6). In 2016, he was awarded the DGA Prize for best Ph.D. by the French Ministry of Higher Education and Research and Ministry of Defence. His main research interests are in Computational Statistics and Machine Learning.

Inhalt

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.