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<font color="#ff8000"> 转移熵 Transfer entropy</font>（也可译为<font color="#ff8000">传递熵</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。<ref>{{cite journal|last=Schreiber|first=Thomas|title=Measuring information transfer|journal=Physical Review Letters|date=1 July 2000|volume=85|issue=2|pages=461–464|doi=10.1103/PhysRevLett.85.461|pmid=10991308|arxiv=nlin/0001042|bibcode=2000PhRvL..85..461S}}</ref><ref name=Scholarpedia >{{cite encyclopedia |year= 2007 |title = Granger causality |volume = 2 |issue = 7 |pages = 1667 |last= Seth |first=Anil|encyclopedia=[[Scholarpedia]] |url=http://www.scholarpedia.org/article/Granger_causality|doi=10.4249/scholarpedia.1667 |bibcode=2007SchpJ...2.1667S|doi-access= free }}</ref><ref name=Schindler07>{{cite journal|last=Hlaváčková-Schindler|first=Katerina|author2=Palus, M |author3=Vejmelka, M |author4= Bhattacharya, J |title=Causality detection based on information-theoretic approaches in time series analysis|journal=Physics Reports|date=1 March 2007|volume=441|issue=1|pages=1–46|doi=10.1016/j.physrep.2006.12.004|bibcode=2007PhR...441....1H|citeseerx=10.1.1.183.1617}}</ref>过程X到过程Y的转移熵是指在给定过去值Y得到过去值X时，Y值不确定性的减少量。更具体地，如果Xt和Yt（t∈N）表示两个随机过程，且信息量用<font color="#ff8000"> 香农熵 Shannon entropy</font>测量，则转移熵可以写为：
<font color="#ff8000"> 转移熵 Transfer entropy</font>（也可译为<font color="#ff8000">传递熵</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。<ref>{{cite journal|last=Schreiber|first=Thomas|title=Measuring information transfer|journal=Physical Review Letters|date=1 July 2000|volume=85|issue=2|pages=461–464|doi=10.1103/PhysRevLett.85.461|pmid=10991308|arxiv=nlin/0001042|bibcode=2000PhRvL..85..461S}}</ref><ref name=Scholarpedia >{{cite encyclopedia |year= 2007 |title = Granger causality |volume = 2 |issue = 7 |pages = 1667 |last= Seth |first=Anil|encyclopedia=[[Scholarpedia]] |url=http://www.scholarpedia.org/article/Granger_causality|doi=10.4249/scholarpedia.1667 |bibcode=2007SchpJ...2.1667S|doi-access= free }}</ref><ref name=Schindler07>{{cite journal|last=Hlaváčková-Schindler|first=Katerina|author2=Palus, M |author3=Vejmelka, M |author4= Bhattacharya, J |title=Causality detection based on information-theoretic approaches in time series analysis|journal=Physics Reports|date=1 March 2007|volume=441|issue=1|pages=1–46|doi=10.1016/j.physrep.2006.12.004|bibcode=2007PhR...441....1H|citeseerx=10.1.1.183.1617}}</ref>过程X到过程Y的转移熵是指在给定过去值Y得到过去值X时，Y值不确定性的减少量。更具体地，如果Xt和Yt（t∈N）表示两个随机过程，且信息量用<font color="#ff8000"> 香农熵 Shannon entropy</font>测量，则转移熵可以写为：
* {{cite web|title=Multivariate Transfer Entropy (MuTE) toolbox|url=https://github.com/montaltoalessandro/MuTE|publisher=[[GitHub]]|date=2019-01-09}}, a toolbox, developed in [[MATLAB]], for computation of transfer entropy with different estimators.
* {{cite web|title=Multivariate Transfer Entropy (MuTE) toolbox|url=https://github.com/montaltoalessandro/MuTE|publisher=[[GitHub]]|date=2019-01-09}}, a toolbox, developed in [[MATLAB]], for computation of transfer entropy with different estimators.
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[[分类: 因果关系]] [[分类: 非线性时间序列分析]] [[分类: 非参数统计]] [[分类: 熵和信息]]
[[分类: 因果关系]] [[分类: 非线性时间序列分析]] [[分类: 非参数统计]] [[分类: 熵和信息]]