Bayesian Inference and Maximum Entropy Methods in Science and Engineering
These proceedings from the 37th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MaxEnt 2017), held in São Carlos, Brazil, aim to expand the available research on Bayesian methods and promote their appli
- PDF / 8,893,349 Bytes
- 306 Pages / 453.543 x 683.15 pts Page_size
- 72 Downloads / 237 Views
Adriano Polpo · Julio Stern Francisco Louzada · Rafael Izbicki Hellinton Takada Editors
Bayesian Inference and Maximum Entropy Methods in Science and Engineering MaxEnt 37, Jarinu, Brazil, July 09–14, 2017
Springer Proceedings in Mathematics & Statistics Volume 239
Springer Proceedings in Mathematics & Statistics This book series features volumes composed of selected contributions from workshops and conferences in all areas of current research in mathematics and statistics, including operation research and optimization. In addition to an overall evaluation of the interest, scientific quality, and timeliness of each proposal at the hands of the publisher, individual contributions are all refereed to the high quality standards of leading journals in the field. Thus, this series provides the research community with well-edited, authoritative reports on developments in the most exciting areas of mathematical and statistical research today.
More information about this series at http://www.springer.com/series/10533
Adriano Polpo Julio Stern Francisco Louzada Rafael Izbicki Hellinton Takada •
•
Editors
Bayesian Inference and Maximum Entropy Methods in Science and Engineering MaxEnt 37, Jarinu, Brazil, July 09–14, 2017
123
Editors Adriano Polpo Department of Statistics Federal University of São Carlos São Carlos, São Paulo Brazil
Rafael Izbicki Department of Statistics Federal University of São Carlos São Carlos, São Paulo Brazil
Julio Stern Applied Mathematics University of São Paulo São Paulo, São Paulo Brazil
Hellinton Takada Itaú Asset Management Banco Itaú-Unibanco São Paulo, São Paulo Brazil
Francisco Louzada Institute of Mathematical Sciences and Computing University of São Paulo São Carlos, São Paulo Brazil
ISSN 2194-1009 ISSN 2194-1017 (electronic) Springer Proceedings in Mathematics & Statistics ISBN 978-3-319-91142-7 ISBN 978-3-319-91143-4 (eBook) https://doi.org/10.1007/978-3-319-91143-4 Library of Congress Control Number: 2018940636 Mathematics Subject Classification (2010): 60G35, 62-06, 62A01, 62F99, 65C40, 65C05, 81P05, 82B31, 82B41, 85A35 © Springer International Publishing AG, part of Springer Nature 2018 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of
Data Loading...
