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→调整后的互信息 Adjusted mutual information
{{Main|adjusted mutual information}}
{{Main|adjusted mutual information}}
A probability distribution can be viewed as a partition of a set. One may then ask: if a set were partitioned randomly, what would the distribution of probabilities be? What would the expectation value of the mutual information be? The adjusted mutual information or AMI subtracts the expectation value of the MI, so that the AMI is zero when two different distributions are random, and one when two distributions are identical. The AMI is defined in analogy to the adjusted Rand index of two different partitions of a set.
A probability distribution can be viewed as a partition of a set. One may then ask: if a set were partitioned randomly, what would the distribution of probabilities be? What would the expectation value of the mutual information be? The adjusted mutual information or AMI subtracts the expectation value of the MI, so that the AMI is zero when two different distributions are random, and one when two distributions are identical. The AMI is defined in analogy to the adjusted Rand index of two different partitions of a set.
概率分布可以被看作是集合划分。然后有人可能会问: 如果一个集合被随机分割，概率的分布会是什么？相互信息的期望值是什么？调整后的互信息或 AMI 减去 MI 的期望值，因此当两个不同的分布是随机的时候 AMI 为零，当两个分布是相同的时候 AMI 为零。Ami 的定义类似于一个集合的两个不同分区的调整后的 Rand 指数。
概率分布可以被看作是集合划分。然后有人可能会问: 如果一个集合被随机分割，概率的分布会是什么？相互信息的期望值是什么？我们用'''<font color="#ff8000">调整后的互信息 Adjusted mutual information</font>'''或 AMI 减去 MI 的期望值，这样当两个不同的分布是随机的时候 AMI 为零，当两个分布是相同的时候 AMI 为零。AMI的定义类似于一个集合的两个不同分区的'''<font color="#ff8000">调整后的Rand指数 Adjusted Rand index</font>'''。
=== 绝对互信息 Absolute mutual information ===<!-- This section is linked from Kolmogorov complexity -->
=== 绝对互信息 Absolute mutual information ===<!-- This section is linked from Kolmogorov complexity -->