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# '''概率、图和因果模型介绍  Introduction to Probabilities, Graphs, and Causal Models'''   
 
# '''概率、图和因果模型介绍  Introduction to Probabilities, Graphs, and Causal Models'''   
 
## 概率论介绍  Introduction to Probability Theory
 
## 概率论介绍  Introduction to Probability Theory
### 为什么需要概率  Why Probabilities?  
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### 为什么需要概率  Why Probabilities?
 
### 概率论的基本概念  Basic Concepts in Probability Theory
 
### 概率论的基本概念  Basic Concepts in Probability Theory
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### Combining Predictive and Diagnostic Supports  1.1.4 Random Variables and Expectations 8  1.1.5 Conditional Independence and Graphoids 11
 
## 图和概率  Graphs and Probabilities
 
## 图和概率  Graphs and Probabilities
 
# '''推断因果理论  A Theory of Inferred Causation'''
 
# '''推断因果理论  A Theory of Inferred Causation'''
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1 Introduction to Probabilities, Graphs, and Causal Models 1
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1.1 Introduction to Probability Theory 1
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1.1.1 Why Probabilities? 1
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1.1.2 Basic Concepts in Probability Theory 2
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1.1.3 Combining Predictive and Diagnostic Supports 6
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1.1.4 Random Variables and Expectations 8
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1.1.5 Conditional Independence and Graphoids 11
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1.2 Graphs and Probabilities 12
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1.2.1 Graphical Notation and Terminology 12
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1.2.2 Bayesian Networks 13
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1.2.3 The d-Separation Criterion 16
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1.2.4 Inference with Bayesian Networks 20
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1.3 Causal Bayesian Networks 21
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1.3.1 Causal Networks as Oracles for Interventions 22
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1.3.2 Causal Relationships and Their Stability 24
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1.4 Functional Causal Models 26
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1.4.1 Structural Equations 27
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1.4.2 Probabilistic Predictions in Causal Models 30
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1.4.3 Interventions and Causal Effects in Functional Models 32
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1.4.4 Counterfactuals in Functional Models 33
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1.5 Causal versus Statistical Terminology 38
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2 A Theory of Inferred Causation 41
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2.1 Introduction – The Basic Intuitions 42
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2.2 The Causal Discovery Framework 43
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2.3 Model Preference (Occam’s Razor) 45
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2.4 Stable Distributions 48
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2.5 Recovering DAG Structures 49
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2.6 Recovering Latent Structures 51
    
== 各章概要 ==
 
== 各章概要 ==
    
==='''概率、图和因果模型介绍'''===
 
==='''概率、图和因果模型介绍'''===
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