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New Trends in Bayesian Statistics

BAYSM 2023, Online Meeting, November 13–17, Selected Contributions

  • Conference proceedings
  • Oct 2025

Overview

Editors:
  1. Alejandra Avalos-Pacheco

    1. Johannes Kepler University Linz, Altenberger Straße , Austria

  2. Fan Bu

    1. University of Michigan, Ann Arbor, USA

  3. Beatrice Franzolini

    1. Università Bocconi, Milan, Italy

  4. Beniamino Hadj-Amar

    1. 6100 Main St, Rice University, Houston - TX 77005, USA

  • Explores cutting-edge Bayesian methods for complex data with theory, computation, and real-world applications
  • Showcases innovative models for dynamic networks, biomedicine, macroeconomics, and privacy-preserving analysis
  • Combines rigorous theoretical guarantees with practical modelling and computational tools

Part of the book series: Springer Proceedings in Mathematics & Statistics (PROMS, volume 511)

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About this book

By integrating cutting-edge statistical research with diverse applications, this book serves as both a reference and an inspiration for those interested in advancing Bayesian methodologies. This volume brings together a collection of research contributions that highlight the versatility and power of Bayesian methods in tackling complex problems across a variety of fields. The chapters reflect the latest advances in Bayesian theory, methodology, and computation, offering novel approaches to analyze data characterized by high dimensionality, structural dependencies, and dynamic behavior. From segmenting mass spectrometry imaging data to modeling dynamic networks and assessing macroeconomic tail risks, this book showcases how advanced Bayesian methods can provide transformative insights while maintaining interpretability and computational feasibility. Whether it’s addressing challenges in biomedicine, where data often come with hierarchical structures and non-standard distributions, or in economics, where time-varying risks demand adaptive models, the contributions in this book demonstrate the unparalleled capacity of Bayesian methods to model, predict, and interpret complex phenomena. Importantly, they also address the need for theoretical guarantees and computational efficiency, making these methods accessible for real-world applications. This volume highlights the versatility of Bayesian methods in tackling diverse, complex problems across disciplines. The chapters reflect the latest advances in statistical theory, computational techniques, and real-world applications. Readers will find innovative solutions for high-dimensional data analysis, clinical trial design, dynamic network modeling, macroeconomic risk assessment, and more. By integrating theory and practice, this book serves as a valuable resource for statisticians, researchers, and practitioners seeking to explore the frontiers of Bayesian inference. 
The volume gathers contributions presented at the Bayesian Young Statisticians Meeting (BAYSM) 2023, the official conference of j-ISBA, the junior section of the International Society for Bayesian Analysis, together with some more invited papers from additional contributors. This prestigious event provides a platform for early-career researchers to showcase innovative work and engage in discussions that shape the future of Bayesian statistics. The inclusion of some additional contributions highlights the vibrancy and creativity of the next generation of Bayesian statisticians, offering a glimpse into cutting-edge methodologies and their diverse applications. The discussions and feedback from BAYSM 2023 have undoubtedly enriched these works, underscoring the collaborative and dynamic nature of the Bayesian research community.

Keywords

  • Bayesian statistics
  • Bayesian nonparametrics
  • Uncertainty quantification
  • Exchangeable partitions
  • Dynamic networks
  • Mass spectrometry imaging
  • Biclustering
  • Graphical models
  • Time-varying risk
  • Dose-escalation trials
  • Gaussian priors
  • Inverse problems
  • Stochastic block models
  • Hierarchical models
  • Variational inference
  • Metropolis-Hastings
  • Statistical machine learning
  • Macroeconomic tail risk
  • Clustering algorithms
  • Health

Editors and Affiliations

  • Johannes Kepler University Linz, Altenberger Straße , Austria

    Alejandra Avalos-Pacheco

  • University of Michigan, Ann Arbor, USA

    Fan Bu

  • Università Bocconi, Milan, Italy

    Beatrice Franzolini

  • 6100 Main St, Rice University, Houston - TX 77005, USA

    Beniamino Hadj-Amar

About the editors

Alejandra Avalos Pacheco is a tenure-track Universitätsassistentin at the Institute of Applied Statistics at JKU Linz, Austria, and an affiliated member of the Harvard-MIT Center for Regulatory Science at Harvard University. She earned her PhD in Statistics through the joint CDT program between the University of Warwick and the University of Oxford. Her thesis received the prestigious Savage Award in Applied Methodology. She has held postdoctoral positions at Harvard University and worked at the Dana-Farber Cancer Institute. Additionally, she served as a research fellow at the University of Florence and a non-tenure-track Universitätsassistentin at TU Wien. Her research focuses on creating interpretable, computationally efficient models for large, complex data, particularly in medical applications such as cancer. She specializes in Bayesian and probabilistic machine learning, with expertise in high-dimensional inference, dimensionality reduction, graphical models, data integration and clinical trials. Fan Bu is a tenure-track Assistant Professor in Biostatistics at the University of Michigan. She completed her Ph.D. in Statistics at Duke University and was previously a postdoctoral research fellow at UCLA, where she developed Bayesian methods for large-scale observational health data. Her research spans Bayesian modeling for temporal and spatio-temporal processes, networks, and federated data, with applications in health data science and observational studies for comparative effectiveness and safety and has appeared in leading journals such as the Journal of the American Statistical Association and Statistics in Medicine. An active member of the Observational Health Data Sciences and Informatics (OHDSI) collaborative, Bu contributes to statistical methods development and leads large-scale network studies to improve health decisions and patient care. Beatrice Franzolini is a Researcher at the Bocconi Institute for Data Science and Analytics, Bocconi University, Milan, Italy. She is a statistician specializing in Bayesian statistical theory, methods and application, with a particular focus on Bayesian nonparametrics. Her research encompasses random probability measures, species sampling models, dependent random partitions, and dynamic models. She has published in leading journals such as Biometrika and The Annals of Applied Statistics. Franzolini holds a Ph.D. from Bocconi University and has held research positions at the Agency for Science, Technology, and Research in Singapore, as well as the Division of Biomedical Data Science at the National University of Singapore's medical school. Beniamino Hadj-Amar is a Postdoctoral Fellow in the Department of Statistics at Rice University, Houston, TX. His research focuses on Bayesian methods for analyzing complex dynamical time series, with expertise in latent structure identification, non-stationary and non-linear processes, and sparse data structures. He holds a Ph.D. from the Oxford-Warwick Statistics Programme (OxWaSP). Hadj-Amar’s methodological toolkit includes switching models, change-point detection, Bayesian nonparametrics, graphical models, and statistical spectral analysis. His work is applied to neuromodulation, respiratory research, and circadian studies, leveraging diverse datasets such as electrophysiological signals, wearable device data, and fMRI.  His contributions have appeared in prestigious journals such as the Journal of the American Statistical Association and The Annals of Applied Statistics.

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Bibliographic Information

  • Book Title: New Trends in Bayesian Statistics

  • Book Subtitle: BAYSM 2023, Online Meeting, November 13–17, Selected Contributions

  • Editors: Alejandra Avalos-Pacheco, Fan Bu, Beatrice Franzolini, Beniamino Hadj-Amar

  • Series Title: Springer Proceedings in Mathematics & Statistics

  • Publisher: Springer Cham

  • eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2026

  • Hardcover ISBN: 978-3-031-99008-3Due: 14 November 2025

  • Softcover ISBN: 978-3-031-99011-3Due: 14 November 2026

  • eBook ISBN: 978-3-031-99009-0Due: 14 November 2025

  • Series ISSN: 2194-1009

  • Series E-ISSN: 2194-1017

  • Edition Number: 1

  • Number of Pages: VIII, 90

  • Number of Illustrations: 2 b/w illustrations, 25 illustrations in colour

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