A framework for adaptive parameter estimation with finite memory
We consider the problem of estimating an unknown parameter from a finite collection of different statistical experiments. The measurements are taken sequentially. Based on the observations made so far, we adaptively select the next experiment that provides the most information about the parameter. Summarizing past information with finite memory, we present a general framework for efficient adaptive estimation, with the sensing schemes fully characterized by finite-state parametric Markov chains. We establish an analytic formula linking the asymptotic performance of adaptive estimation schemes to the steady-state distributions of the associated Markov chains. Consequently, finding optimal adaptive strategies can be reformulated as the problem of designing a (continuous) family of Markov chains with prescribed steady-state distributions. We also propose a quantitative design criterion for optimal sensing policies based on minimax ratio regret.