Overview
- Original contributions from BAYSM 2014 researchers cover recent developments in Bayesian statistics
- Includes academic as well as industrial research and applications of Bayesian statistics
- Incorporates input from renowned plenary lecturers and senior discussants
Part of the book series: Springer Proceedings in Mathematics & Statistics (PROMS, volume 126)
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Table of contents (14 papers)
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Front Matter
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Theory and Methods
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Front Matter
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Applications and Case Studies
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Front Matter
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Back Matter
Keywords
About this book
The Second Bayesian Young Statisticians Meeting (BAYSM 2014) and the research presented here facilitate connections among researchers using Bayesian Statistics by providing a forum for the development and exchange of ideas. WU Vienna University of Business and Economics hosted BAYSM 2014 from September 18th to the 19th. The guidance of renowned plenary lecturers and senior discussants is a critical part of the meeting and this volume, which follows publication of contributions from BAYSM 2013. The meeting's scientific program reflected the variety of fields in which Bayesian methods are currently employed or could be introduced in the future. Three brilliant keynote lectures by Chris Holmes (University of Oxford), Christian Robert (Université Paris-Dauphine), and Mike West (Duke University), were complemented by 24 plenary talks covering the major topics Dynamic Models, Applications, Bayesian Nonparametrics, Biostatistics, Bayesian Methods in Economics, and Models and Methods, as well as a lively poster session with 30 contributions. Selected contributions have been drawn from the conference for this book. All contributions in this volume are peer-reviewed and share original research in Bayesian computation, application, and theory.
Editors and Affiliations
About the editors
Sylvia Frühwirth-Schnatter is a Professor of Applied Statistics and Econometrics at the Department of Finance, Accounting, and Statistics at the WU Vienna University of Economics and Business, Austria. She received her PhD in Mathematics from the Vienna University of Technology in 1988. She has published in many leading journals in applied statistics and econometrics on topics such as Bayesian inference, finite mixture models, Markov switching models, state space models, and their application in economics, finance, and business. In 2014, she became elected member of the Austrian Academy of Science.
Angela Bitto holds a Masters in Mathematics and is currently working on her PhD in Statistics at the Vienna University of Technology. Her research focuses on the Bayesian estimation of sparse time-varying parameter models. Prior to joining the Institute of Statistics and Mathematics at the WU Vienna University of Economics and Business, she worked as a research analyst for the European Central Bank.
Gregor Kastner is an Assistant Professor at the WU Vienna University of Economics and Business and a Lecturer at the University of Applied Sciences in Wiener Neustadt, Austria. He holds Masters in Mathematics, Computer Science, Informatics Management, and Physical Education; in 2014 he received his PhD in Mathematics. Gregor researches the Bayesian modeling of economic time series, in particular the efficient estimation of univariate and high-dimensional stochastic volatility models. His work has been published in leading journals in computational statistics and computer software.
Alexandra Posekany is an Assistant Professor at the Institute of Statistics and Mathematics, WU Vienna University of Economics and Business, Austria. She holds a PhD in Mathematics from the Vienna University of Technology. Her research includes applications of Bayesian analysis in computational biology and econometrics, as well as the development of algorithms andstatistical methods in Bayesian computing and big data analysis.
Bibliographic Information
Book Title: Bayesian Statistics from Methods to Models and Applications
Book Subtitle: Research from BAYSM 2014
Editors: Sylvia Frühwirth-Schnatter, Angela Bitto, Gregor Kastner, Alexandra Posekany
Series Title: Springer Proceedings in Mathematics & Statistics
DOI: https://doi.org/10.1007/978-3-319-16238-6
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer International Publishing Switzerland 2015
Hardcover ISBN: 978-3-319-16237-9Published: 02 June 2015
Softcover ISBN: 978-3-319-36234-2Published: 09 October 2016
eBook ISBN: 978-3-319-16238-6Published: 19 May 2015
Series ISSN: 2194-1009
Series E-ISSN: 2194-1017
Edition Number: 1
Number of Pages: XIII, 167
Number of Illustrations: 15 b/w illustrations, 25 illustrations in colour
Topics: Statistical Theory and Methods, Statistics for Business, Management, Economics, Finance, Insurance, Statistics and Computing/Statistics Programs