Directed information is an information theory measure that quantifies the information flow from the random string X n = ( X 1 , X 2 , … , X n ) {\displaystyle X^{n}=(X_{1},X_{2},\dots ,X_{n})} to the random string Y n = ( Y 1 , Y 2 , … , Y n ) {\displaystyle Y^{n}=(Y_{1},Y_{2},\dots ,Y_{n})} . The term directed information was coined by James Massey and is defined as
where I ( X i ; Y i | Y i − 1 ) {\displaystyle I(X^{i};Y_{i}|Y^{i-1})} is the conditional mutual information I ( X 1 , X 2 , . . . , X i ; Y i | Y 1 , Y 2 , . . . , Y i − 1 ) {\displaystyle I(X_{1},X_{2},...,X_{i};Y_{i}|Y_{1},Y_{2},...,Y_{i-1})} .
Directed information has applications to problems where causality plays an important role such as the capacity of channels with feedback, capacity of discrete memoryless networks, capacity of networks with in-block memory, gambling with causal side information, compression with causal side information, real-time control communication settings, and statistical physics.