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For example, the deterministic mapping <math>\{(1,1),(2,2),(3,3)\}</math> may be viewed as stronger than the deterministic mapping <math>\{(1,3),(2,1),(3,2)\}</math>, although these relationships would yield the same mutual information.  This is because the mutual information is not sensitive at all to any inherent ordering in the variable values ({{harvnb|Cronbach|1954}}, {{harvnb|Coombs|Dawes|Tversky|1970}}, {{harvnb|Lockhead|1970}}), and is therefore not sensitive at all to the '''form''' of the relational mapping between the associated variables.  If it is desired that the former relation—showing agreement on all variable values—be judged stronger than the later relation, then it is possible to use the following ''weighted mutual information'' {{harv|Guiasu|1977}}.

For example, the deterministic mapping <math>\{(1,1),(2,2),(3,3)\}</math> may be viewed as stronger than the deterministic mapping <math>\{(1,3),(2,1),(3,2)\}</math>, although these relationships would yield the same mutual information.  This is because the mutual information is not sensitive at all to any inherent ordering in the variable values ({{harvnb|Cronbach|1954}}, {{harvnb|Coombs|Dawes|Tversky|1970}}, {{harvnb|Lockhead|1970}}), and is therefore not sensitive at all to the '''form''' of the relational mapping between the associated variables.  If it is desired that the former relation—showing agreement on all variable values—be judged stronger than the later relation, then it is possible to use the following ''weighted mutual information'' {{harv|Guiasu|1977}}.

For example, the deterministic mapping <math>\{(1,1),(2,2),(3,3)\}</math> may be viewed as stronger than the deterministic mapping <math>\{(1,3),(2,1),(3,2)\}</math>, although these relationships would yield the same mutual information. This is because the mutual information is not sensitive at all to any inherent ordering in the variable values (, , ), and is therefore not sensitive at all to the form of the relational mapping between the associated variables. If it is desired that the former relation—showing agreement on all variable values—be judged stronger than the later relation, then it is possible to use the following weighted mutual information .

For example, the deterministic mapping {(1,1),(2,2),(3,3)} may be viewed as stronger than the deterministic mapping {(1,3),(2,1),(3,2)}, although these relationships would yield the same mutual information. This is because the mutual information is not sensitive at all to any inherent ordering in the variable values, and is therefore not sensitive at all to the form of the relational mapping between the associated variables. If it is desired that the former relation—showing agreement on all variable values—be judged stronger than the later relation, then it is possible to use the following weighted mutual information.

例如，确定性映射<math>\{(1,1),(2,2),(3,3)\}</math>可能被视为比确定性映射数学<math>\{(1,3),(2,1),(3,2)\}</math>更强，尽管这些关系产生的互信息是相同的。这是因为互信息对变量值(，,)的任何固有顺序都不敏感，因此对相关变量之间的关系映射形式一点也不敏感。如果希望判断前一个关系(即对所有变量值的一致性)比后一个关系强，则可以使用下列加权互信息。

例如，确定性映射<math>\{(1,1),(2,2),(3,3)\}</math>可能被视为比确定性映射数学<math>\{(1,3),(2,1),(3,2)\}</math>更强，尽管这些关系产生的互信息是相同的。这是因为互信息对变量值(，,)的任何固有顺序都不敏感，因此对相关变量之间的关系映射形式一点也不敏感。如果希望判断前一个关系(即对所有变量值的一致性)比后一个关系强，则可以使用下列加权互信息。

= \sum_{y \in Y} \sum_{x \in X} w(x,y) p(x,y) \log \frac{p(x,y)}{p(x)\,p(y)}, </math>

:<math> \operatorname{I}(X;Y) = \sum_{y \in Y} \sum_{x \in X} w(x,y) p(x,y) \log \frac{p(x,y)}{p(x)\,p(y)}, </math>