[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-216827-en":3,"doc-seo-216827-105":30,"detail-sidebar-cat-0-en-105":97},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},216827,962084928904,"Jake","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Bayesian Inference and Maximum Entropy Methods in Science and Engineering - Issue 37 - Preface","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.","Springer Proceedings in Mathematics & Statistics  \nAdriano Polpo · Julio Stern Francisco Louzada · Rafael Izbicki Hellinton Takada Editors  \nBayesian Inference and Maximum Entropy Methods in Science and Engineering  \nMaxEnt 37, Jarinu, Brazil, July 09–14, 2017  \nSpringer Proceedings in Mathematics & Statistics  \nVolume 239  \nSpringer Proceedings in Mathematics & Statistics  \nThis 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.  \nMore information about this series at [http://www.springer.com/series/10533](http://www.springer.com/series/10533)  \nAdriano Polpo • Julio Stern Francisco Louzada • Rafael Izbicki Hellinton Takada  \nEditors  \nBayesian Inference and Maximum Entropy Methods in Science and Engineering  \nMaxEnt 37, Jarinu, Brazil, July 09–14, 2017  \nEditors  \nAdriano Polpo  \nDepartment of Statistics Federal University of São Carlos São Carlos, São Paulo  \nBrazil  \nJulio Stern  \nApplied Mathematics University of São Paulo São Paulo, São Paulo Brazil  \nFrancisco Louzada  \nInstitute of Mathematical Sciences and Computing  \nUniversity of São Paulo São Carlos, São Paulo Brazil  \nRafael Izbicki  \nDepartment of Statistics Federal University of São Carlos São Carlos, São Paulo  \nBrazil  \nHellinton Takada  \nItaú Asset Management Banco Itaú-Unibanco São Paulo, São Paulo Brazil  \nISSN 2194-1009 ISSN 2194-1017 (electronic)  \nSpringer Proceedings in Mathematics & Statistics  \nISBN 978-3-319-91142-7 ISBN 978-3-319-91143-4 (eBook)  \n[https://doi.org/10.1007/978-3-319-91143-4](https://doi.org/10.1007/978-3-319-91143-4)  \nLibrary of Congress Control Number: 2018940636  \nMathematics Subject Classification (2010): 60G35, 62-06, 62A01, 62F99, 65C40, 65C05, 81P05, 82B31, 82B41, 85A35  \n© Springer International Publishing AG, part of Springer Nature 2018  \nThis 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.  \nThe 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.  \nThe 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.  \nPrinted on acid-free paper  \nThis Springer imprint is published by the registered company Springer International Publishing AG part of Springer Nature  \nThe registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland  \nPreface  \nThis book brings the contributed works of MaxEnt 2017—37th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering ([http://www","cbCaimzreUe1j9SL","https://ap.wps.com/l/cbCaimzreUe1j9SL","pdf",8893349,1,306,"English","en",105,"# Preface\n## Workshop objectives and themes\n## Bayesian computational techniques and foundations\n## Organization and committees","[{\"question\":\"What event does this book’s preface introduce?\",\"answer\":\"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.\"},{\"question\":\"What topics does the workshop emphasize?\",\"answer\":\"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.\"},{\"question\":\"Which types of application areas are mentioned?\",\"answer\":\"It lists applications across multiple fields such as astronomy, reliability, cosmology, econometrics, engineering, quantum mechanics, stochastic processes, survival, and more.\"}]","Bayesian Inference and Maximum Entropy Methods in Science and Engineering - 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