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Transfer entropy is a non-parametric statistic measuring the amount of directed (time-asymmetric) transfer of information between two random processes. Transfer entropy from a process X to another process Y is the amount of uncertainty reduced in future values of Y by knowing the past values of X given past values of Y. More specifically, if <math> X_t </math> and <math> Y_t </math> for <math> t\in \mathbb{N} </math> denote two random processes and the amount of information is measured using Shannon's entropy, the transfer entropy can be written as:
Transfer entropy is a non-parametric statistic measuring the amount of directed (time-asymmetric) transfer of information between two random processes. Transfer entropy from a process X to another process Y is the amount of uncertainty reduced in future values of Y by knowing the past values of X given past values of Y. More specifically, if <math> X_t </math> and <math> Y_t </math> for <math> t\in \mathbb{N} </math> denote two random processes and the amount of information is measured using Shannon's entropy, the transfer entropy can be written as:
<font color="#ff8000"> 传递熵Transfer entropy</font>是衡量两个随机过程之间有向（时间不对称）信息传递量的非参数统计量。从一个过程X到另一个过程Y的传递熵是通过知道给定Y的过去值X的过去值而在Y的未来值中减少的不确定性量。更具体地说，如果t∈N的Xt和Yt表示两个随机过程，并且信息量是用香农熵测量的，则传递熵可以写成：
where H(X) is Shannon entropy of X. The above definition of transfer entropy has been extended by other types of entropy measures such as Rényi entropy.
where H(X) is Shannon entropy of X. The above definition of transfer entropy has been extended by other types of entropy measures such as Rényi entropy.
其中 h (x)是 x 的 Shannon 熵。上述转移熵的定义被其他类型的熵测度(如 r é nyi 熵)所扩展。
其中 H (x)是 x 的香农熵。上述转移熵的定义被其他类型的熵测度(如Rényi熵)所扩展。