Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Update 5.1 Bayes Network.md #35

Open
wants to merge 1 commit into
base: master
Choose a base branch
from
Open

Update 5.1 Bayes Network.md #35

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
2 changes: 1 addition & 1 deletion Machine Learning/5.1 Bayes Network/5.1 Bayes Network.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -19,7 +19,7 @@

概率图中的节点分为隐含节点和观测节点，边分为有向边和无向边。从概率论的角度，节点对应于随机变量，边对应于随机变量的依赖或相关关系，其中**有向边表示单向的依赖，无向边表示相互依赖关系**。

概率图模型分为**贝叶斯网络（Bayesian Network）和马尔可夫网络（Markov Network）**两大类。贝叶斯网络可以用一个有向图结构表示，马尔可夫网络可以表 示成一个无向图的网络结构。更详细地说，概率图模型包括了朴素贝叶斯模型、最大熵模型、隐马尔可夫模型、条件随机场、主题模型等，在机器学习的诸多场景中都有着广泛的应用。
概率图模型分为**贝叶斯网络（Bayesian Network）和马尔可夫网络（Markov Network）**两大类。贝叶斯网络可以用一个有向图结构表示，马尔可夫网络可以表示成一个无向图的网络结构。更详细地说，概率图模型包括了朴素贝叶斯模型、最大熵模型、隐马尔可夫模型、条件随机场、主题模型等，在机器学习的诸多场景中都有着广泛的应用。

## 2. 细数贝叶斯网络

Expand Down