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'''Transfer entropy''' is a [[non-parametric statistics|non-parametric statistic]] measuring the amount of directed (time-asymmetric) transfer of [[information]] between two [[random process]]es.<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> 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 statistics|non-parametric statistic]] measuring the amount of directed (time-asymmetric) transfer of [[information]] between two [[random process]]es.<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> 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: