- 概率图模型(原理与应用全彩英文版香农信息科学经典)
- - 作者：(墨)路易斯·恩里克·苏卡|责编:陈亮//刘叶青
  - 出版社：世图出版公司
  - ISBN：9787519296957
  - 出版日期：2023/01/01
  - 页数：253
- 售价：51.6

内容大纲
本书从工程的角度概述了概率图模型（PGMs）。书本涵盖了PGMs每种主要类别的基础知识，包括表示、推理和学习原则，并回顾了每种类型的模型在现实世界中的应用。这些应用来自广泛的学科，突出了贝叶斯分类器、隐马尔可夫模型、贝叶斯网络、动态和时间贝叶斯网络、马尔可夫随机场、影响图和马尔可夫决策过程的许多用途。本书特色：提出了包括PGMs所有主要类别的统一框架；介绍了不同技术的实际应用；该领域研究的最新发展，包括多维贝叶斯分类器、关系图模型和因果模型；每一章的末尾都附有练习、进一步阅读的建议和研究或编程项。
作者介绍
目录
Part I  Fundamentals
  1  Introduction
    1.1  Uncertainty
      1.1.1  Effects of Uncertainty
    1.2  A Brief History
    1.3  Basic Probabilistic Models
      1.3.1  An Example
    1.4  Probabilistic Graphical Models
    1.5  Representation, Inference, and Learning
    1.6  Applications
    1.7  Overview of the Book
    1.8  Additional Reading
    References
  2  Probability Theory
    2.1  Introduction
    2.2  Basic Rules
    2.3  Random Variables
      2.3.1  Two-Dimensional Random Variables
    2.4  Information Theory
    2.5  Additional Reading
    2.6  Exercises
  Reference
  3  Graph Theory
    3.1  Definitions
    3.2  Types of Graphs
    3.3  Trajectories and Circuits
    3.4  Graph Isomorphism
    3.5  Trees
    3.6  Cliques
    3.7  Perfect Ordering
    3.8  Ordering and Triangulation Algorithms
      3.8.1  Maximum Cardinality Search
      3.8.2  Graph Filling
    3.9  Additional Reading
    3.10  Exercises
  Reference
Part II  Probabilistic Models
  4  Bayesian Classifiers
    4.1  Introduction
      4.1.1  Classifier Evaluation
    4.2  Bayesian Classifier
      4.2.1  Naive Bayes Classifier
    4.3  Alternative Models: TAN, BAN
    4.4  Semi-Naive Bayesian Classifiers
    4.5  Multidimensional Bayesian Classifiers
      4.5.1  Multidimensional Bayesian Network Classifiers
      4.5.2  Bayesian Chain Classifiers
    4.6  Hierarchical Classification
      4.6.1  Chained Path Evaluation
    4.7  Applications

      4.7.1  Visual Skin Detection
      4.7.2  HIV Drug Selection
    4.8  Additional Reading
    4.9  Exercises
    References
  5  Hidden Markov Models
    5.1  Introduction
    5.2  Markov Chains
      5.2.1  Parameter Estimation
      5.2.2  Convergence
    5.3  Hidden Markov Models
      5.3.1  Evaluation
      5.3.2  State Estimation
      5.3.3  Learning
      5.3.4  Extensions
    5.4  Applications
      5.4.1  PageRank
      5.4.2  Gesture Recognition
    5.5  Additional Reading
    5.6  Exercises
    References
  6  Markov Random Fields
    6.1  Introduction
    6.2  Markov Networks
      6.2.1  Regular Markov Random Fields
    6.3  Gibbs Random Fields
    6.4  Inference
    6.5  Parameter Estimation
      6.5.1  Parameter Estimation with Labeled Data
    6.6  Conditional Random Fields
    6.7  Applications
      6.7.1  Image Smoothing
      6.7.2  Improving Image Annotation
    6.8  Additional Reading
    6.9  Exercises
    References
  7  Bayesian Networks: Representation and Inference
    7.1  Introduction
    7.2  Representation
      7.2.1  Structure
      7.2.2  Parameters
    7.3  Inference
      7.3.1  Singly Connected Networks: Belief Propagation
      7.3.2  Multiple Connected Networks
      7.3.3  Approximate Inference
      7.3.4  Most Probable Explanation
      7.3.5  Continuous Variables
    7.4  Applications
      7.4.1  Information Validation
      7.4.2  Reliability Analysis

    7.5  Additional Reading
    7.6  Exercises
    References
  8  Bayesian Networks: Learning
    8.1  Introduction
    8.2  Parameter Learning
      8.2.1  Smoothing
      8.2.2  Parameter Uncertainty
      8.2.3  Missing Data
      8.2.4  Discretization
    8.3  Structure Learning
      8.3.1  Tree Learning
      8.3.2  Learning a Polytree
      8.3.3  Search and Score Techniques
      8.3.4  Independence Tests Techniques
    8.4  Combining Expert Knowledge and Data
    8.5  Applications
      8.5.1  Air Pollution Model for Mexico City
    8.6  Additional Reading
    8.7  Exercises
    References
  9  Dynamic and Temporal Bayesian Networks
    9.1  Introduction
    9.2  Dynamic Bayesian Networks
      9.2.1  Inference
      9.2.2  Learning
    9.3  Temporal Event Networks
      9.3.1  Temporal Nodes Bayesian Networks
    9.4  Applications
      9.4.1  DBN: Gesture Recognition
      9.4.2  TNBN: Predicting HIV Mutational Pathways
    9.5  Additional Reading
    9.6  Exercises
    References
Part III  Decision Models
  10  Decision Graphs
    10.1  Introduction
    10.2  Decision Theory
      10.2.1  Fundamentals
    10.3  Decision Trees
    10.4  Influence Diagrams
      10.4.1  Modeling
      10.4.2  Evaluation
      10.4.3  Extensions
    10.5  Applications
      10.5.1  Decision-Theoretic Caregiver
    10.6  Additional Reading
    10.7  Exercises
    References
  11  Markov Decision Processes

    11.1  Introduction
    11.2  Modeling
    11.3  Evaluation
      11.3.1  Value Iteration
      11.3.2  Policy Iteration
    11.4  Factored MDPs
      11.4.1  Abstraction
      11.4.2  Decomposition
    11.5  Partially Observable Markov Decision Processes
    11.6  Applications
      11.6.1  Power Plant Operation
      11.6.2  Robot Task Coordination
    11.7  Additional Reading
    11.8  Exercises
    References
Part IV  Relational and Causal Models
  12  Relational Probabilistic Graphical Models
    12.1  Introduction
    12.2  Logic
      12.2.1  Propositional Logic
      12.2.2  First-Order Predicate Logic
    12.3  Probabilistic Relational Models
      12.3.1  Inference
      12.3.2  Learning
    12.4  Markov Logic Networks
      12.4.1  Inference
      12.4.2  Learning
    12.5  Applications
      12.5.1  Student Modeling
    12.6  Probabilistic Relational Student Model
      12.6.1  Visual Grammars
    12.7  Additional Reading
    12.8  Exercises
    Reference
  13  Graphical Causal Models
    13.1  Introduction
    13.2  Causal Bayesian Networks
    13.3  Causal Reasoning
      13.3.1  Prediction
      13.3.2  Counterfactuals
    13.4  Learning Causal Models
    13.5  Applications
      13.5.1  Learning a Causal Model for ADHD
    13.6  Additional Reading
    13.7  Exercises
    References
Glossary
Index

内容大纲

作者介绍

目录

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