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第971行： − + − − When <math>X</math> and <math>Y</math> are limited to be in a discrete number of states, observation data is summarized in a contingency table, with row variable <math>X</math> (or <math>i</math>) and column variable <math>Y</math> (or <math>j</math>). Mutual information is one of the measures of association or correlation between the row and column variables. Other measures of association include Pearson's chi-squared test statistics, G-test statistics, etc. In fact, mutual information is equal to G-test statistics divided by <math>2N</math>, where <math>N</math> is the sample size. − − 当数学 x / 数学和数学 y / 数学被限制在一个离散的数字状态时，观察数据被总结成一个列联表，有行变量 math x / math (或 math i / math)和列变量 math y / math (或 math j / math)。互信息是行变量和列变量之间关联或相关性的度量之一。其他衡量相关性的指标包括皮尔森卡方检验统计数据、 g 测试统计数据等。事实上，互信息等于 g 检验统计除以 math 2N / math，其中 math n / math 是样本量。 − − + +
→对于离散数据 For discrete data
=== 对于离散数据 For discrete data ===
=== 对于离散数据 For discrete data ===
When <math>X</math> and <math>Y</math> are limited to be in a discrete number of states, observation data is summarized in a [[contingency table]], with row variable <math>X</math> (or <math>i</math>) and column variable <math>Y</math> (or <math>j</math>). Mutual information is one of the measures of [[association (statistics)|association]] or [[correlation and dependence|correlation]] between the row and column variables. Other measures of association include [[Pearson's chi-squared test]] statistics, [[G-test]] statistics, etc. In fact, mutual information is equal to [[G-test]] statistics divided by <math>2N</math>, where <math>N</math> is the sample size.
When <math>X</math> and <math>Y</math> are limited to be in a discrete number of states, observation data is summarized in a [[contingency table]], with row variable <math>X</math> (or <math>i</math>) and column variable <math>Y</math> (or <math>j</math>). Mutual information is one of the measures of [[association (statistics)|association]] or [[correlation and dependence|correlation]] between the row and column variables. Other measures of association include [[Pearson's chi-squared test]] statistics, [[G-test]] statistics, etc. In fact, mutual information is equal to [[G-test]] statistics divided by <math>2N</math>, where <math>N</math> is the sample size.
When 𝑋 and 𝑌 are limited to be in a discrete number of states, observation data is summarized in a contingency table, with row variable 𝑋 (or 𝑖) and column variable 𝑌 (or 𝑗). Mutual information is one of the measures of association or correlation between the row and column variables. Other measures of association include Pearson's chi-squared test statistics, G-test statistics, etc. In fact, mutual information is equal to G-test statistics divided by 2𝑁, where 𝑁 is the sample size.
当<math>X</math>和<math>Y</math>被限制为离散状态时，观测数据汇总在'''<font color="#ff8000">列联表 Contingency table</font>'''中，列变量为行变量<math>X</math>（或<math>i</math>）和列变量<math>Y</math>（或<math>j</math>）。互信息是行和列变量之间关联或相关性的度量之一。其他关联度量包括Pearson卡方检验统计量、'''<font color="#ff8000">G检验 G-test</font>'''统计量等。事实上，互信息等于G检验统计量除以<math>2N</math>，其中<math>N</math>为样本量。
== 应用 Applications ==
== 应用 Applications ==