ProbabilisticGraphicalModelsPrinciplesandTechniques-其它文档类资源

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Daphne Koller 关于Probabilistic Graphical Models 的最权威大作,内容详实深入,是各大名校机器学习和人工智能专业相应课程的指定教材
Adaptive Computation and Machine LearningThomas dietterich, EditorChristopher Bishop, David Heckerman, Michael Jordan, and Michael Kearns, Associate EditorsBioinformatics: The Machine learning Approach, Pierre Baldi and Soren BrunakReinforcement Learning: An Introduction, Richard S. Sutton and Andrew G. BartoGraphical models for Machine Learning and Digital Communication, Brendan J. FreyLearning in graphical Models, Michael I. JordanCausation, Prediction, and Search, 2nd ed, Peter Spirtes, Clark Glymour, and Richard ScheinesPrinciples of Data Mining, David Hand, Heikki Mannila, and Padhraic SmythBioinformatics: The Machine Learning Approach, 2nd ed, Pierre Baldi and Soren BrunakLearning Kernel classifiers: Theory and Algorithms, Ralf HerbrichLearning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, Bernhard Scholkopf and Alexander J smolaIntroduction to Machine Learning, Ethem AlpaydinGaussian Processes for Machine Learning, Carl Edward Rasmussen and Christopher K.I. WilliamsSemi-Supervised Learning, Olivier Chapelle, Bernhard Scholkopf, and Alexander Zien, edsThe Minimum description Length Principle, Peter D GrunwaldIntroduction to Statistical Relational Learning, lise Getoor and Ben Taskar, edsProbabilistic Graphical Models: Principles and Techniques, Daphne Koller and Nir FriedmanProbabilistic Graphical ModelsPrinciples and TechniquesDaphne kollerNir friedmanThe mit pressCambridge, MassachusettsLondon, England@2009 Massachusetts Institute of TechnologyAll rights reserved. No part of this book may be reproduced in any form by any electronicor mechanical means (including photocopying, recording, or information storage and retrieval)without permission in writing from the publisherFor information about special quantity discounts, please email special_sales@mitpress. mit. eduThis book was set by the authors in BlFX2EPrinted and bound in the united states of americaLibrary of Congress Cataloging-in-Publication DataKoller, DaphneProbabilistic Graphical Models: Principles and Techniques Daphne Koller and Nir Friedmanp cm. -(Adaptive computation and machine learning)Includes bibliographical references and indexisBn 978-0-262-01319-2(hardcover: alk. paper1. Graphical modeling(Statistics) 2. Bayesian statistical decision theory--Graphic methods. IKoller, Daphne. II. Friedman, NirQA279.5.K652010519.5’420285-dc222009008615109876543To our familiesmy parents Dov and ditzamy husband danmy daughters natalie and mayaDKmy parents Noga and Gadmy wifemy children roy and liorMEAs far as the laws of mathematics refer to reality, they are not certain, as far as they arecertain, they do not refer to realityAlbert einstein 1956When we try to pick out anything by itself, we find that it is bound fast by a thousandinvisible cords that cannot be broken, to everything in the universeJohn Muir, 1869The 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 areasonable man's mindJames Clerk Maxwell, 1850The theory of probabilities is at bottom nothing but common sense reduced to calculus;itenables us to appreciate with exactness that which accurate minds feel with a sort of instinctfor which ofttimes they are unable to account.Pierre Simon Laplace, 1819Misunderstanding of probability may be the greatest of all impediments to scientific literacyStephen Jay GouldContentsAcknowledgmentsList of figuresList of algorithmsList of boxesXXX1 IntroductionL1 Motivation 11.2 Structured Probabilistic Models 21.2.1 Probabilistic Graphical Models 31.2.2 Representation, Inference, Learning 51.3 Overview and roadmap 61.3.1 Overview of Chapters 61.3.2Readers guide1.3.3 Connection to Other Disciplines1.4 Historical notes 122 Foundations2. 1 Probability Theory2. 1. 1 Probability Distributions 152. 1.2 Basic Concepts in Probability 182. 1.3 Random Variables and Joint Distributions 192. 1.4 Independence and Conditional Independence 2:2. 1.5 Querying a Distribution2. 1.6 Continuous Spaces 272. 1.7 Expectation and Variance 312.2 Graphs 342.2.1Nodes and edges 342.2.2 Subgraphs 352.2.3 Paths and trails 36

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评论信息

  • herovsdevil :
    太好了,终于找到了我想要的资源
    2018-08-30
  • renshengabco :
    非常好的经典书籍!
    2017-12-02
  • laozhu1232 :
    太好了,终于找到了我想要的资源
    2017-09-19
  • henglufang :
    概率图模型领域最经典的一本书,而且非常难懂。
    2017-09-07
  • B-Kalasiky :
    很好,好厚的书
    2016-11-22

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