Probabilistic Graphical Models - Principles and Techniques

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英文名:Mit - Probabilistic Graphical Models - Principles and Techniques.pdf, 中文名:概率图模型,概率图论模型 机器学习选用教材
Adaptive Computation and Machine Learning Thomas Dietterich Editor Christopher Bishop, David Heckerman, Michael Jordan, and Michael Kearns, Associate Editors Bioinformatics: The Machine Learning Approach, Pierre Baldi and Soren Brunak Reinforcement Learning: An Introduction, Richard S Sutton and Andrew G Barto Graphical Models for Machine learning and Digital Communication, Brendan J. Frey Learning in Graphical Models, Michael I. Jordan Causation, Prediction, and Search, 2nd ed, Peter Spirtes, Clark Glymour, and Richard Scheines Principles of Data Mining, David Hand, Heikki Mannila, and Padhraic Smyth Bioinformatics: The Machine learning approach, 2nd ed, Pierre Baldi and Soren Brunak Learning Kernel classifiers: Theory and Algorithms, Ralf Herbrich Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, Bern- hard Scholkopf and Alexander J Smola Introduction to Machine Learning, Ethem alpaydin Gaussian Processes for Machine Learning, Carl Edward Rasmussen and Christopher K I. williams Semi-Supervised Learning, Olivier Chapelle, Bernhard Scholkopf, and Alexander Zien, eds The Minimum Description Length Principle, Peter D. Grunwald Introduction to Statistical Relational Learning, Lise Getoor and Ben Taskar, eds 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 02009 Massachusetts Institute of Technology All rights reserved. No part of this book may be reproduced in any form by any electronic 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 KTFX2E 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 QA279.5K652010 5195420285-dc22 2009008615 98765 To our families my parents Dov and Itza ly husband do my daughters Natalie and maya DK my parents Noga and Gad ife Yael my children roy and Lior ME As far as the laws of mathematics refer to reality, they are not certain, as far as they are certain, they do not refer to reality Albert einstein, 1956 When we try to pick out anything by itself we find that it is bound fast by a thousand invisible cords that cannot be broken, to everything in the universe John muir, 1869 The actual science of logic is conversant at present only with things either certain, impossible, or entirely doubtful .. Therefore the true logic for this world is the calculus of probabilities, which takes account of the magnitude of the probability which is, or ought to be, in a reasonable man's mind James Clerk Maxwell, 1850 The theory of probabilities is at bottom nothing but common sense reduced to calculus; it enables us to appreciate with exactness that which accurate minds feel with a sort of instinct for which ofttimes they are unable to account. Pierre Simon Laplace, 1819 Misunderstanding of probability may be the greatest of all impediments to scientific literacy Stephen Jay Gould Contents Acknowledgments List of figures XXV List of algorithms List of boxes 1 Introduction 11 Motivation 1.2 Structured Probabilistic Models 2 1.2.1 Probabilistic Graphical Models 3 1.2.2 Representation, Inference, Learning 5 1.3 Overview and Roadmap 6 1.3.1 Overview of Chapters 6 13.2 Reader's guide 9 1.3.3 Connection to Other Disciplines 11 1.4 Historical notes 12 2 Foundations 2. 1 Probability the 2.1.1 Probability distributions 2.1.2 Basic Concepts in Probability 18 2.1.3 Random Variables and Joint Distributions 19 2. 1.4 Independence and Conditional Independence 23 Querying a Distrib 25 2.1.6 Continuous spaces 27 2. 1.7 Expectation and Variance 31 2.2 Graphs 34 2.2.1 Nodes and edges 2.2.2 Subgraphs 35 2. 2.3 Paths and trails 36

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