Computational Probability Algorithms and Applications in the Mathema
This new edition includes the latest advances and developments in computational probability involving A Probability Programming Language (APPL). The book examines and presents, in a systematic manner, computational probability methods that encompass data
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John H. Drew Diane L. Evans Andrew G. Glen Lawrence M. Leemis
Computational Probability Algorithms and Applications in the Mathematical Sciences Second Edition
International Series in Operations Research & Management Science Volume 246
Series Editor Camille C. Price Stephen F. Austin State University, TX, USA Associate Series Editor Joe Zhu Worcester Polytechnic Institute, MA, USA Founding Series Editor Frederick S. Hillier Stanford University, CA, USA
More information about this series at http://www.springer.com/series/6161
John H. Drew • Diane L. Evans • Andrew G. Glen Lawrence M. Leemis
Computational Probability Algorithms and Applications in the Mathematical Sciences Second Edition
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John H. Drew Department of Mathematics The College of William and Mary Williamsburg, VA, USA
Diane L. Evans Department of Mathematics Rose-Hulman Institute of Technology Terre Haute, IN, USA
Andrew G. Glen Department of Mathematics and Computer Science Colorado College Colorado Springs, CO, USA
Lawrence M. Leemis Department of Mathematics The College of William and Mary Williamsburg, VA, USA
ISSN 0884-8289 ISSN 2214-7934 (electronic) International Series in Operations Research & Management Science ISBN 978-3-319-43321-9 ISBN 978-3-319-43323-3 (eBook) DOI 10.1007/978-3-319-43323-3 Library of Congress Control Number: 2016961255 © Springer International Publishing AG 2008, 2017 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 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. Printed on acid-free paper This Springer imprint is published by Springer Nature The registered company is Springer International Publishing AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
Preface
For decades, statisticians have enjoyed the use of “statistical packages” which read in a (potentially) large data set, calculate numerical summaries such as the sample mean and sample variance, calculate more sophisticated statistical quantities such as confidence interval bounds and p-values associated with hypothesis tests, and
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