抄録
"We formulate an optimal estimation process in a stochastic growth model with an unknown true probability model. When the only available information is a sample realization generated by a stationary and ergodic stochastic process, we prove that the optimal estimation process based on likelihood-?hspace{0pt}increasing behavior converges to the true probability measure. Moreover, we show that the likelihood-?hspace{0pt}increasing estimate (LIE) defines a transition function on the sample space, and the probability measure that dominates an estimation set is an invariant distribution of the LIE."
