134
个编辑
更改
跳到导航
跳到搜索
小第14行：
第14行： − + 第21行：
第21行： − + + + + + + 第27行：
第32行： − + − + − + − + − + − + 第59行：
第64行： − + − + − + − + − + − + − + − + 第469行：
第474行： − + 第475行：
第480行： − + 第491行：
第496行： − + − − == 各章概要 == − − ==='''概率、图和因果模型介绍'''===
1.1.1和1.1.2，感觉找到处理这种任务的想法了
* '''[http://bayes.cs.ucla.edu/BOOK-2K/ 在线网站]''' 含有习题、勘误、问题讨论等资源
* '''[http://bayes.cs.ucla.edu/BOOK-2K/ 在线网站]''' 含有习题、勘误、问题讨论等资源
== 书籍目录 ==
== 目录与概要 ==
'''<big>1 概率、图和因果模型介绍 Introduction to Probabilities, Graphs, and Causal Models 1</big>'''
'''<big>1 概率、图和因果模型介绍 Introduction to Probabilities, Graphs, and Causal Models 1</big>'''
1.1.1 为什么需要概率 Why Probabilities? 1
1.1.1 为什么需要概率 Why Probabilities? 1
1.1.2 概率论的一些基本概念 Basic Concepts in Probability Theory 2
* 因果论断的发生具有不确定性，比如“心不在焉的开车会导致车祸”，前因会让后果更容易发生，但不是绝对的。
* 与断言逻辑相比，基于概率的表达更容易处理，不然断言需要考虑到大量使其不成立的特例。
1.1.2 概率论中的基本概念 Basic Concepts in Probability Theory 2
* 介绍有关概率论中离散变量的相关基础知识，并聚焦于贝叶斯推理的角度。
1.1.3 Combining Predictive and Diagnostic Supports 6
1.1.3 Combining Predictive and Diagnostic Supports 6
1.1.4 随机变量与数学期望 Random Variables and Expectations 8
1.1.4 随机变量与数学期望 Random Variables and Expectations 8
1.1.5 条件独立与Conditional Independence and Graphoids 11
1.1.5 条件独立与Graphoid Conditional Independence and Graphoids 11
'''1.2 图和概率 Graphs and Probabilities 12'''
'''1.2 图和概率 Graphs and Probabilities 12'''
1.2.1 Graphical Notation and Terminology 12
1.2.1 图的记号与术语 Graphical Notation and Terminology 12
1.2.2 Bayesian Networks 13
1.2.2 贝叶斯网络 Bayesian Networks 13
1.2.3 The d-Separation Criterion 16
1.2.3 d-分离准则 The d-Separation Criterion 16
1.2.4 Inference with Bayesian Networks 20
1.2.4 贝叶斯网络推断 Inference with Bayesian Networks 20
'''1.3 Causal Bayesian Networks 21'''
'''1.3 因果贝叶斯网络 Causal Bayesian Networks 21'''
1.3.1 Causal Networks as Oracles for Interventions 22
1.3.1 Causal Networks as Oracles for Interventions 22
'''<big>2 推断因果理论 A Theory of Inferred Causation 41</big>'''
'''<big>2 推断因果理论 A Theory of Inferred Causation 41</big>'''
'''2.1 Introduction – The Basic Intuitions 42'''
'''2.1 介绍—基本的直觉 Introduction – The Basic Intuitions 42'''
'''2.2 The Causal Discovery Framework 43'''
'''2.2 因果发现框架 The Causal Discovery Framework 43'''
'''2.3 Model Preference (Occam’s Razor) 45'''
'''2.3 模型偏好（奥卡姆剃刀） Model Preference (Occam's Razor) 45'''
'''2.4 Stable Distributions 48'''
'''2.4 稳定分布 Stable Distributions 48'''
'''2.5 Recovering DAG Structures 49'''
'''2.5 发现DAG结构 Recovering DAG Structures 49'''
'''2.6 Recovering Latent Structures 51'''
'''2.6 滞后结构再发现 Recovering Latent Structures 51'''
'''2.7 Local Criteria for Inferring Causal Relations 54'''
'''2.7 因果关系推断的局部准则 Local Criteria for Inferring Causal Relations 54'''
'''2.8 Nontemporal Causation and Statistical Time 57'''
'''2.8 Nontemporal Causation and Statistical Time 57'''
'''2.9 Conclusions 59'''
'''2.9 总结 Conclusions 59'''
2.9.1 On Minimality, Markov, and Stability 61
2.9.1 On Minimality, Markov, and Stability 61
'''11.6 Decisions and Confounding (Chapter 6) 380'''
'''11.6 Decisions and Confounding (Chapter 6) 380'''
11.6.1 Simpson’s Paradox and Decision Trees 380
11.6.1 Simpson's Paradox and Decision Trees 380
11.6.2 Is Chronological Information Sufficient for Decision Trees? 382
11.6.2 Is Chronological Information Sufficient for Decision Trees? 382
11.6.3 Lindley on Causality, Decision Trees, and Bayesianism 384
11.6.3 Lindley on Causality, Decision Trees, and Bayesianism 384
11.6.4 Why Isn’t Confounding a Statistical Concept? 387
11.6.4 Why Isn't Confounding a Statistical Concept? 387
'''11.7 The Calculus of Counterfactuals 389'''
'''11.7 The Calculus of Counterfactuals 389'''
'''11.9 More on Probabilities of Causation 396'''
'''11.9 More on Probabilities of Causation 396'''
11.9.1 Is “Guilty with Probability One” Ever Possible? 396
11.9.1 Is "Guilty with Probability One" Ever Possible? 396
11.9.2 Tightening the Bounds on Probabilities of Causation 398
11.9.2 Tightening the Bounds on Probabilities of Causation 398