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Daphne Koller_Probabilistic Graphical Models

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Most tasks require a person or an automated system to reason--to reach conclusions based on available information. The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms.

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Probabilistic Graphical Models: Principles and Techniques, Daphne Koller and Nir Friedman

Probabilistic Graphical Models

Principles and Techniques

Daphne Koller

Nir Friedman

The MIT Press

Cambridge, Massachusetts

London, England

or mechanical means (including photocopying, recording, or information storage and retrieval)

without permission in writing from the publisher.

For information about special quantity discounts, please email special_sales@mitpress.mit.edu

This book was set by the authors in L



Printed and bound in the United States of America.

Library of Congress Cataloging-in-Publication Data

Koller, Daphne.

Probabilistic Graphical Models: Principles and Techniques / Daphne Koller and Nir Friedman.

p. cm. – (Adaptive computation and machine learning)

Includes bibliographical references and index.

ISBN 978-0-262-01319-2 (hardcover : alk. paper)

1. Graphical modeling (Statistics) 2. Bayesian statistical decision theory—Graphic methods. I.

Koller, Daphne. II. Friedman, Nir.

QA279.5.K65 2010

519.5’420285–dc22

2009008615

109876543

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