Editors:
- 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)
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Front Matter
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Surveys
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Front Matter
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Real World Case Studies in Health
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Front Matter
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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.
Keywords
Editors and Affiliations
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Mathematical Sciences, Queensland University of Technology, Brisbane, Australia
Kerrie L. Mengersen
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I2M, CNRS, Centrale Marseille, Aix-Marseille University, Marseille, France
Pierre Pudlo
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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