Overview
- Highlights novel methodological and computational contributions on Bayesian statistics
- Presents successful applications of Bayesian statistics in neuroscience, astrostatistics and climate change
- Provides new findings and research questions to stimulate future advances in Bayesian statistics
Part of the book series: Springer Proceedings in Mathematics & Statistics (PROMS, volume 296)
Included in the following conference series:
Conference proceedings info: BAYSM 2018.
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Table of contents (18 papers)
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Front Matter
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Theory and Methods
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Front Matter
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Computational Statistics
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Front Matter
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Applied Statistics
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Front Matter
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Other volumes
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Bayesian Statistics and New Generations
Keywords
- 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
About this book
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.
Editors and Affiliations
About the editors
Raffaele Argiento is an Assistant Professor of Statistics at the Department of Economic, Social, Mathematical and Statistical Sciences (ESOMAS), University of Turin, Italy. He is member of the board for the Ph.D. in Modeling and Data Science at the same University and affiliated to the “de Castro” Statistics initiative hosted by the Collegio Carlo Alberto, Turin. His research focuses on Bayesian parametric and nonparametric methods from both theoretical and applied viewpoints. He is the executive director of the Applied Bayesian Summer School (ABS) and a member of the BAYSM board.
Daniele Durante is an Assistant Professor of Statistics at the Department of Decision Sciences, Bocconi University, Italy, and a Research Affiliate at the Bocconi Institute for Data Science and Analytics (BIDSA). His research is characterized by its use of an interdisciplinary approach at the intersection of Bayesian methods, modern applications, and statistical learning to develop flexible and computationally tractable models for handling complex data. He was the chair of the Junior Section of the International Society for Bayesian Analysis (j-ISBA) in 2018.
Sara Wade is a Lecturer in Statistics and Data Science at the School of Mathematics, University of Edinburgh, UK. Prior to this, she was a Harrison Early Career Assistant Professor of Statistics at the University of Warwick, UK, where she organised and chaired the 4th BAYSM. Her research focuses on Bayesian nonparametrics and machine learning, especially the development of flexible nonparametric priors and efficient inference for complex data.
Bibliographic Information
Book Title: Bayesian Statistics and New Generations
Book Subtitle: BAYSM 2018, Warwick, UK, July 2-3 Selected Contributions
Editors: Raffaele Argiento, Daniele Durante, Sara Wade
Series Title: Springer Proceedings in Mathematics & Statistics
DOI: https://doi.org/10.1007/978-3-030-30611-3
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer Nature Switzerland AG 2019
Hardcover ISBN: 978-3-030-30610-6Published: 22 November 2019
Softcover ISBN: 978-3-030-30613-7Published: 22 November 2020
eBook ISBN: 978-3-030-30611-3Published: 21 November 2019
Series ISSN: 2194-1009
Series E-ISSN: 2194-1017
Edition Number: 1
Number of Pages: XI, 184
Number of Illustrations: 11 b/w illustrations, 29 illustrations in colour
Topics: Statistical Theory and Methods, Statistics and Computing/Statistics Programs, Statistics for Business, Management, Economics, Finance, Insurance, Statistics for Life Sciences, Medicine, Health Sciences, Simulation and Modeling