Dynamics of the Fisher information metric.
We present a method to generate probability distributions that correspond to metrics obeying partial differential equations generated by extremizing a functional J [g(mu nu) (theta(i)) ] , where g(mu nu) (theta(i)) is the Fisher metric. We postulate that this functional of the dynamical variable g(mu nu) (theta(i)) is stationary with respect to small variations of these variables. Our approach enables a dynamical approach to the Fisher information metric. It allows one to impose symmetries on a statistical system in a systematic way.
Calmet
Xavier
X
University of North Carolina, Chapel Hill, North Carolina 27599, USA. calmet@physics.unc.edu
Calmet
Jacques
J