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Case Studies in Applied Bayesian Data Science

CIRM Jean-Morlet Chair, Fall 2018

  • Book
  • © 2020

Overview

Editors:
  1. Kerrie L. Mengersen

    1. Mathematical Sciences, Queensland University of Technology, Brisbane, Australia

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  2. Pierre Pudlo

    1. I2M, CNRS, Centrale Marseille, Aix-Marseille University, Marseille, France

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    You can also search for this editor in PubMed   Google Scholar

  3. Christian P. Robert

    1. CEREMADE, Université Paris Dauphine, Paris, France

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    You can also search for this editor in PubMed   Google Scholar

  • Presents a survey of state of the art aspects of applied Bayesian data science
  • Presents real-world case studies in applied Bayesian data science in the fields of health and ecology
  • Introduces new methodologies

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

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Table of contents (17 chapters)

  1. Front Matter

    Pages i-vi
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  2. Surveys

    1. Front Matter

      Pages 1-1
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    2. Introduction

      • Kerrie L. Mengersen, Pierre Pudlo, Christian P. Robert
      Pages 3-15

    3. A Survey of Bayesian Statistical Approaches for Big Data

      • Farzana Jahan, Insha Ullah, Kerrie L. Mengersen
      Pages 17-44

    4. Bayesian Neural Networks: An Introduction and Survey

      • Ethan Goan, Clinton Fookes
      Pages 45-87

    5. Markov Chain Monte Carlo Algorithms for Bayesian Computation, a Survey and Some Generalisation

      • Wu Changye, Christian P. Robert
      Pages 89-119

    6. Bayesian Variable Selection

      • Matthew Sutton
      Pages 121-135

    7. Bayesian Computation with Intractable Likelihoods

      • 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
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    2. A Bayesian Hierarchical Approach to Jointly Model Cortical Thickness and Covariance Networks

      • Marcela I. Cespedes, James M. McGree, Christopher C. Drovandi, Kerrie L. Mengersen, Lee B. Reid, James D. Doecke et al.
      Pages 155-213

    3. Bayesian Spike Sorting: Parametric and Nonparametric Multivariate Gaussian Mixture Models

      • Nicole White, Zoé van Havre, Judith Rousseau, Kerrie L. Mengersen
      Pages 215-227

    4. Spatio-Temporal Analysis of Dengue Fever in Makassar Indonesia: A Comparison of Models Based on CARBayes

      • Aswi Aswi, Susanna Cramb, Wenbiao Hu, Gentry White, Kerrie L. Mengersen
      Pages 229-244

    5. A Comparison of Bayesian Spatial Models for Cancer Incidence at a Small Area Level: Theory and Performance

      • Susanna Cramb, Earl Duncan, Peter Baade, Kerrie L. Mengersen
      Pages 245-274

    6. An Ensemble Approach to Modelling the Combined Effect of Risk Factors on Age at Parkinson’s Disease Onset

      • Aleysha Thomas, Paul Wu, Nicole M. White, Leisa Toms, George Mellick, Kerrie L. Mengersen
      Pages 275-302

    7. Workplace Health and Workplace Wellness: Synergistic or Disconnected?

      • G. Davis, E. Moloney, M. da Palma, Kerrie L. Mengersen, F. Harden
      Pages 303-326

    8. Bayesian Modelling to Assist Inference on Health Outcomes in Occupational Health Surveillance

      • 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
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    2. Bayesian Networks for Understanding Human-Wildlife Conflict in Conservation

      • Jac Davis, Kyle Good, Vanessa Hunter, Sandra Johnson, Kerrie L. Mengersen
      Pages 347-370

    3. Bayesian Learning of Biodiversity Models Using Repeated Observations

      • Ana M. M. Sequeira, M. Julian Caley, Camille Mellin, Kerrie L. Mengersen
      Pages 371-384

    4. Thresholds of Coral Cover That Support Coral Reef Biodiversity

      • Julie Vercelloni, M. Julian Caley, Kerrie L. Mengersen
      Pages 385-398
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Keywords

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. 

Editors and Affiliations

  • Mathematical Sciences, Queensland University of Technology, Brisbane, Australia

    Kerrie L. Mengersen

  • I2M, CNRS, Centrale Marseille, Aix-Marseille University, Marseille, France

    Pierre Pudlo

  • CEREMADE, Université Paris Dauphine, Paris, France

    Christian P. Robert

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

  • DOI: https://doi.org/10.1007/978-3-030-42553-1

  • 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 2020

  • Softcover ISBN: 978-3-030-42552-4Published: 29 May 2020

  • eBook ISBN: 978-3-030-42553-1Published: 28 May 2020

  • 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, http://smf.emath.fr; ISBN SMF: [to follow]

  • Topics: Bayesian Inference, Probability Theory and Stochastic Processes, Applied Statistics

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