Information flow in an information theoretical context is the transfer of information from a variable h to a variable l in a given process. The measure of information flow, p, is defined as the uncertainty before the process started minus the uncertainty after the process has terminated. This can be quantified as
- Failed to parse (unknown function\stackrel): H(h|l) - H(h|l')\ \stackrel{\mathrm{def}}{=}\ (H(h,l) - H(l)) - (H(h,l') - H(l'))\,\!
where H(h | l) is the conditional entropy (equivocation) of variable h (before the process started) given the variable l (before the process started), and H(h | l') is the conditional entropy (equivocation) of variable h (before the process started) given the variable l' (the value of variable l after the process finished).
H(X,Y) is the joint entropy, and can be calculated as follows:
This entry is from Wikipedia, the leading user-contributed encyclopedia. It may not have been reviewed by professional editors (see full disclaimer)
