Mutual information between discrete and continuous data sets.
Mutual information between discrete and continuous data sets.
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DOI：
10.1371/journal.pone.0087357
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发表时间：
2014
期刊：
影响因子：
3.7
通讯作者：
Ross BC
中科院分区：
文献类型：
--
作者：
Ross BC
互信息（MI）是检测数据集之间关系的一种有效方法。当两个数据集都是离散的或两个数据集都是连续的时，存在用于估计MI的精确方法，其避免了“分箱”问题。我们针对一个离散数据集和一个连续数据集的情况提出了一个准确的、非分组的MI估计器。这种情况适用于测量例如碱基序列和基因表达水平之间的关系，或癌症药物对患者存活时间的影响。我们还展示了我们的方法如何适用于计算两个或多个数据集的Jensen-Shannon散度。
Mutual information (MI) is a powerful method for detecting relationships between data sets. There are accurate methods for estimating MI that avoid problems with “binning” when both data sets are discrete or when both data sets are continuous. We present an accurate, non-binning MI estimator for the case of one discrete data set and one continuous data set. This case applies when measuring, for example, the relationship between base sequence and gene expression level, or the effect of a cancer drug on patient survival time. We also show how our method can be adapted to calculate the Jensen–Shannon divergence of two or more data sets.
影响因子：
2.4
作者：
Grosse, I;Bernaola-Galván, P;Stanley, HE
通讯作者：
Stanley, HE
影响因子：
2.4
作者：
Kraskov, A;Stögbauer, H;Grassberger, P
通讯作者：
Grassberger, P