  We propose a computational method for large deviation statistics of
time-averaged quantities in general Markov processes. In our proposed method,
we repeat a response measurement against external forces, where the forces are
determined by the previous measurement as feedback. Consequently, we obtain a
set of stationary states corresponding to an exponential family of
distributions, each of which shows rare events in the original system as the
typical behavior. As a demonstration of our method, we study large deviation
statistics of one-dimensional lattice gas models.
