Bayesian Statistics and New Generations

BAYSM 2018, Warwick, UK, July 2-3 Selected Contributions

  • Raffaele Argiento
  • Daniele Durante
  • Sara Wade
Conference proceedings BAYSM 2018

Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 296)

Table of contents

  1. Front Matter
    Pages i-xi
  2. Theory and Methods

    1. Front Matter
      Pages 1-1
    2. Nishma Laitonjam, Neil Hurley
      Pages 23-34
    3. Laura Fee Schneider, Thomas Staudt, Axel Munk
      Pages 35-42
    4. Cedric Spire, Dalia Chakrabarty
      Pages 43-51
    5. Daniel J. Tait, Bruce J. Worton
      Pages 53-61
  3. Computational Statistics

    1. Front Matter
      Pages 63-63
    2. Nathan Cunningham, Jim E. Griffin, David L. Wild, Anthony Lee
      Pages 65-74
    3. Gilles Kratzer, Reinhard Furrer, Marta Pittavino
      Pages 95-104
    4. Iliana Peneva, Richard S. Savage
      Pages 105-114
  4. Applied Statistics

    1. Front Matter
      Pages 123-123
    2. Kristian Brock, Lucinda Billingham, Christina Yap, Gary Middleton
      Pages 125-133
    3. Ettore Lanzarone, Elisa Scalco, Alfonso Mastropietro, Simona Marzi, Giovanna Rizzo
      Pages 135-144
    4. Diana Rocha, Manuel Scotto, Carla Pinto, João Tavares, Sónia Gouveia
      Pages 145-154
    5. Aditi Shenvi, Jim Q. Smith, Robert Walton, Sandra Eldridge
      Pages 155-163
    6. Oliver G. Stevenson, Brendon J. Brewer
      Pages 165-173
    7. Fiona Turner, Richard Wilkinson, Caitlin Buck, Julie Jones, Louise Sime
      Pages 175-182
  5. Back Matter
    Pages 183-184

About these proceedings


This book presents a selection of peer-reviewed contributions to the fourth Bayesian Young Statisticians Meeting, BAYSM 2018, held at the University of Warwick on 2-3 July 2018. The meeting provided a valuable opportunity for young researchers, MSc students, PhD students, and postdocs interested in Bayesian statistics to connect with the broader Bayesian community. The proceedings offer cutting-edge papers on a wide range of topics in Bayesian statistics, identify important challenges and investigate promising methodological approaches, while also assessing current methods and stimulating applications. The book is intended for a broad audience of statisticians, and demonstrates how theoretical, methodological, and computational aspects are often combined in the Bayesian framework to successfully tackle complex problems.


Bayesian Modeling Data Science Bayesian Computation Methodological and Applied Statistics Neurosciences, astrostatistics, climate change Bayesian inference Proceedings Parametric inference Nonparametric inference Inference from stochastic processes Applications Computational problems in statistics Multivariate analysis Young researchers

Editors and affiliations

  • Raffaele Argiento
    • 1
  • Daniele Durante
    • 2
  • Sara Wade
    • 3
  1. 1.Department of Statistical SciencesUniversità Cattolica del Sacro CuoreMilanItaly
  2. 2.Department of Decision SciencesBocconi UniversityMilanItaly
  3. 3.School of MathematicsUniversity of EdinburghEdinburghUK

Bibliographic information