Bayesian Statistics and New Generations : BAYSM 2018, Warwick, UK, July 2-3 Selected Contributions

Raffaele Argiento

ISBN 10: 3030306135 / ISBN 13: 9783030306137

Editore: Springer International Publishing, 2020

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Druck auf Anfrage Neuware - Printed after ordering - 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.

Codice articolo 9783030306137

Dati bibliografici

• Titolo: Bayesian Statistics and New Generations : BAYSM 2018, Warwick, UK, July 2-3 Selected Contributions
• Editore: Springer International Publishing
• Data di pubblicazione: 2020
• Lingua: Inglese
• ISBN 10: 3030306135
• ISBN 13: 9783030306137
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 235 L x 155 A x 11 P (millimetri) 235 millimetri di larghezza per 155 millimetri di altezza per 11 millimetri di profondità
• Peso: 348 grammi

Descrizione articolo

Riassunto

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.

Informazioni sull?autore

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.