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第1行： − 此词条暂由彩云小译翻译，未经人工整理和审校，带来阅读不便，请见谅。+ 第9行：
第9行： − 图模型或概率图模型(PGM)或结构化概率模型是一种用图表示随机变量之间条件依赖结构的概率模型。它们通常用于概率论、统计学---- 尤其是贝叶斯统计学---- 和机器学习。+ 第17行：
第17行： − 一个图形模型的例子。每个箭头表示一个依赖项。在这个例子中: d 依赖于 a、 b 和 c; c 依赖于 b 和 d; 而 a 和 b 各自独立。+ + 第27行：
第28行： − 一般来说，概率图模型使用基于图的表示作为对多维空间上的分布进行编码的基础，而图是一组独立分布的紧凑或分解表示。分布的图形表示常用的两个分支，即贝叶斯网络和马尔可夫随机场。这两个族都包含因子分解和独立性的性质，但是它们在它们可以编码的独立性集合和它们所诱导的分布的因子分解上有所不同。 09{ cite book+
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此词条暂由Yuling翻译，未经人工整理和审校，带来阅读不便，请见谅。
{{machine learning bar}}
{{machine learning bar}}
A graphical model or probabilistic graphical model (PGM) or structured probabilistic model is a probabilistic model for which a graph expresses the conditional dependence structure between random variables. They are commonly used in probability theory, statistics—particularly Bayesian statistics—and machine learning.
A graphical model or probabilistic graphical model (PGM) or structured probabilistic model is a probabilistic model for which a graph expresses the conditional dependence structure between random variables. They are commonly used in probability theory, statistics—particularly Bayesian statistics—and machine learning.
'''<font color="#ff8000">图模型 Graphical Model</font>'''或'''<font color="#ff8000">概率图模型 Probabilistic Graphical Model</font>'''(PGM)或结构化概率模型是一种用图表示随机变量之间条件依赖关系的概率模型。它们通常用于概率论、统计学---- 尤其是贝叶斯统计学---- 和'''<font color="#ff8000">机器学习 Machine Learning</font>'''。
An example of a graphical model. Each arrow indicates a dependency. In this example: D depends on A, B, and C; and C depends on B and D; whereas A and B are each independent.
An example of a graphical model. Each arrow indicates a dependency. In this example: D depends on A, B, and C; and C depends on B and D; whereas A and B are each independent.
这是一个图模型的例子。每个箭头表示一个依赖关系。在这个例子中: D 依赖于 A、 B 和 C; C 依赖于 B 和 D; 而 A 和 B 相互独立。
==Types of graphical models==
==Types of graphical models==
图模型的类别
Generally, probabilistic graphical models use a graph-based representation as the foundation for encoding a distribution over a multi-dimensional space and a graph that is a compact or [[Factor graph|factorized]] representation of a set of independences that hold in the specific distribution. Two branches of graphical representations of distributions are commonly used, namely, [[Bayesian network]]s and [[Markov random field]]s. Both families encompass the properties of factorization and independences, but they differ in the set of independences they can encode and the factorization of the distribution that they induce.<ref name=koller09>{{cite book
Generally, probabilistic graphical models use a graph-based representation as the foundation for encoding a distribution over a multi-dimensional space and a graph that is a compact or [[Factor graph|factorized]] representation of a set of independences that hold in the specific distribution. Two branches of graphical representations of distributions are commonly used, namely, [[Bayesian network]]s and [[Markov random field]]s. Both families encompass the properties of factorization and independences, but they differ in the set of independences they can encode and the factorization of the distribution that they induce.<ref name=koller09>{{cite book
Generally, probabilistic graphical models use a graph-based representation as the foundation for encoding a distribution over a multi-dimensional space and a graph that is a compact or factorized representation of a set of independences that hold in the specific distribution. Two branches of graphical representations of distributions are commonly used, namely, Bayesian networks and Markov random fields. Both families encompass the properties of factorization and independences, but they differ in the set of independences they can encode and the factorization of the distribution that they induce.<ref name=koller09>{{cite book
Generally, probabilistic graphical models use a graph-based representation as the foundation for encoding a distribution over a multi-dimensional space and a graph that is a compact or factorized representation of a set of independences that hold in the specific distribution. Two branches of graphical representations of distributions are commonly used, namely, Bayesian networks and Markov random fields. Both families encompass the properties of factorization and independences, but they differ in the set of independences they can encode and the factorization of the distribution that they induce.<ref name=koller09>{{cite book
一般来说，概率图模型使用将图的表示方法作为对多维空间上的分布进行编码的基础，而图是一组独立分布的紧凑或分解表示。常用的概率图模型大致分为两类，即贝叶斯网络和马尔可夫随机场。这两个族都包含因子分解和独立性的性质，但是它们在它们可以编码的独立性集合和它们所诱导的分布的因子分解上有所不同。 09{ cite book
|author=Koller, D.
|author=Koller, D.