Bayesian Inference and Maximum Entropy Methods in Science and Engineering - Issue 37 - Preface

Sep 8, 2026

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Bayesian Inference and Maximum Entropy Methods in Science and Engineering assembles refereed contributions from MaxEnt 2017, the 37th International Workshop held in Jarinu, Brazil (July 9–14, 2017). The preface frames the workshop’s goal of strengthening Bayesian research and maximum entropy methods while extending applications across diverse scientific communities, from astronomy and reliability to econometrics, engineering, quantum mechanics, and stochastic processes. Emphasis is placed on Bayesian computational techniques, probabilistic foundations, information theory, and the discussion of new applications informed by inference foundations of physical theories.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages306

Reading Time12 h 51 min

AccessFree to download

Preface

Workshop objectives and themes

Bayesian computational techniques and foundations

Organization and committees

Springer Proceedings in Mathematics & Statistics 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](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 Editors Adriano Polpo 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 Francisco Louzada Institute of Mathematical Sciences and Computing University of São Paulo São Carlos, São Paulo Brazil Rafael Izbicki Department of Statistics Federal University of São Carlos São Carlos, São Paulo Brazil Hellinton Takada Itaú Asset Management Banco Itaú-Unibanco São Paulo, 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](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](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 publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional afﬁliations. Printed on acid-free paper This Springer imprint is published by the registered company Springer International Publishing AG part of Springer Nature The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland Preface This book brings the contributed works of MaxEnt 2017—37th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering ([http://www

FAQs

What event does this book’s preface introduce?

It introduces MaxEnt 2017, the 37th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, held in Jarinu, Brazil, July 9–14, 2017.

What topics does the workshop emphasize?

The preface highlights Bayesian methods and maximum entropy, Bayesian computational techniques (including Monte Carlo Markov Chain and approximate inferential methods), probability foundations, and information theory.

Which types of application areas are mentioned?

It lists applications across multiple fields such as astronomy, reliability, cosmology, econometrics, engineering, quantum mechanics, stochastic processes, survival, and more.

Sep 8, 2026

Upload by Jake