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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?
### 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
### 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'''
1 Introduction to Probabilities, Graphs, and Causal Models 1
1.1 Introduction to Probability Theory 1
1.1.1 Why Probabilities? 1
1.1.2 Basic Concepts in Probability Theory 2
1.1.3 Combining Predictive and Diagnostic Supports 6
1.1.4 Random Variables and Expectations 8
1.1.5 Conditional Independence and Graphoids 11
1.2 Graphs and Probabilities 12
1.2.1 Graphical Notation and Terminology 12
1.2.2 Bayesian Networks 13
1.2.3 The d-Separation Criterion 16
1.2.4 Inference with Bayesian Networks 20
1.3 Causal Bayesian Networks 21
1.3.1 Causal Networks as Oracles for Interventions 22
1.3.2 Causal Relationships and Their Stability 24
1.4 Functional Causal Models 26
1.4.1 Structural Equations 27
1.4.2 Probabilistic Predictions in Causal Models 30
1.4.3 Interventions and Causal Effects in Functional Models 32
1.4.4 Counterfactuals in Functional Models 33
1.5 Causal versus Statistical Terminology 38
2 A Theory of Inferred Causation 41
2.1 Introduction – The Basic Intuitions 42
2.2 The Causal Discovery Framework 43
2.3 Model Preference (Occam’s Razor) 45
2.4 Stable Distributions 48
2.5 Recovering DAG Structures 49
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'''===