© 2020

Case Studies in Applied Bayesian Data Science

CIRM Jean-Morlet Chair, Fall 2018

  • Kerrie L. Mengersen
  • Pierre Pudlo
  • Christian P. Robert

Part of the Lecture Notes in Mathematics book series (LNM, volume 2259)

Table of contents

  1. Front Matter
    Pages i-vi
  2. Surveys

    1. Front Matter
      Pages 1-1
    2. Kerrie L. Mengersen, Pierre Pudlo, Christian P. Robert
      Pages 3-15
    3. Farzana Jahan, Insha Ullah, Kerrie L. Mengersen
      Pages 17-44
    4. Ethan Goan, Clinton Fookes
      Pages 45-87
    5. Matthew Sutton
      Pages 121-135
    6. Matthew T. Moores, Anthony N. Pettitt, Kerrie L. Mengersen
      Pages 137-151
  3. Real World Case Studies in Health

    1. Front Matter
      Pages 153-153
    2. Marcela I. Cespedes, James M. McGree, Christopher C. Drovandi, Kerrie L. Mengersen, Lee B. Reid, James D. Doecke et al.
      Pages 155-213
    3. Nicole White, Zoé van Havre, Judith Rousseau, Kerrie L. Mengersen
      Pages 215-227
    4. Aswi Aswi, Susanna Cramb, Wenbiao Hu, Gentry White, Kerrie L. Mengersen
      Pages 229-244
    5. Susanna Cramb, Earl Duncan, Peter Baade, Kerrie L. Mengersen
      Pages 245-274
    6. Aleysha Thomas, Paul Wu, Nicole M. White, Leisa Toms, George Mellick, Kerrie L. Mengersen
      Pages 275-302
    7. G. Davis, E. Moloney, M. da Palma, Kerrie L. Mengersen, F. Harden
      Pages 303-326
    8. Nicholas J. Tierney, Samuel Clifford, Christopher C. Drovandi, Kerrie L. Mengersen
      Pages 327-343
  4. Real World Case Studies in Ecology

    1. Front Matter
      Pages 345-345
    2. Jac Davis, Kyle Good, Vanessa Hunter, Sandra Johnson, Kerrie L. Mengersen
      Pages 347-370
    3. Ana M. M. Sequeira, M. Julian Caley, Camille Mellin, Kerrie L. Mengersen
      Pages 371-384
    4. Julie Vercelloni, M. Julian Caley, Kerrie L. Mengersen
      Pages 385-398

About this book


Presenting a range of substantive applied problems within Bayesian Statistics along with their Bayesian solutions, this book arises from a research program at CIRM in France in the second semester of 2018, which supported Kerrie Mengersen as a visiting Jean-Morlet Chair and Pierre Pudlo as the local Research Professor.

The field of Bayesian statistics has exploded over the past thirty years and is now an established field of research in mathematical statistics and computer science, a key component of data science, and an underpinning methodology in many domains of science, business and social science. Moreover, while remaining naturally entwined, the three arms of Bayesian statistics, namely modelling, computation and inference, have grown into independent research fields.While the research arms of Bayesian statistics continue to grow in many directions, they are harnessed when attention turns to solving substantive applied problems. Each such problem set has its own challenges and hence draws from the suite of research a bespoke solution.

The book will be useful for both theoretical and applied statisticians, as well as practitioners, to inspect these solutions in the context of the problems, in order to draw further understanding, awareness and inspiration. 


Applied Data Science Applied Statistics Bayesian Statistics Case Studies in Data Science Bayesian neural networks Big data Bayesian computation Spatial models Case studies in Ecology Mixture models Case studies in Health Bayesian Optimization composite likelihood Markov random fields

Editors and affiliations

  • Kerrie L. Mengersen
    • 1
  • Pierre Pudlo
    • 2
  • Christian P. Robert
    • 3
  1. 1.Mathematical SciencesQueensland University of TechnologyBrisbaneAustralia
  2. 2.I2M, CNRS, Centrale MarseilleAix-Marseille UniversityMarseilleFrance
  3. 3.CEREMADEUniversité Paris DauphineParisFrance

Bibliographic information

  • Book Title Case Studies in Applied Bayesian Data Science
  • Book Subtitle CIRM Jean-Morlet Chair, Fall 2018
  • Editors Kerrie L. Mengersen
    Pierre Pudlo
    Christian P. Robert
  • Series Title Lecture Notes in Mathematics
  • Series Abbreviated Title Lect.Notes Mathematics
  • DOI
  • Copyright Information The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020
  • Publisher Name Springer, Cham
  • eBook Packages Mathematics and Statistics Mathematics and Statistics (R0)
  • Softcover ISBN 978-3-030-42552-4
  • eBook ISBN 978-3-030-42553-1
  • Series ISSN 0075-8434
  • Series E-ISSN 1617-9692
  • Edition Number 1
  • Number of Pages VI, 420
  • Number of Illustrations 16 b/w illustrations, 94 illustrations in colour
  • Additional Information Jointly published with Société Mathématique de France (SMF); sold and distributed to its memebers by the SMF,; ISBN SMF: [to follow]
  • Topics Bayesian Inference
    Probability Theory and Stochastic Processes
    Applied Statistics
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