Adaptive Designs for Optimal Observed Fisher Information
针对观测Fisher信息无法先验已知的问题，提出两种利用先前试验观测信息指导后续试验的自适应设计方法，以改进最大似然估计的方差近似。
Summary Expected Fisher information can be found a priori and as a result its inverse is the primary variance approximation used in the design of experiments. This is in contrast with the common claim that the inverse of the observed Fisher information is a better approximation of the variance of the maximum likelihood estimator. Observed Fisher information cannot be known a priori; however, if an experiment is conducted sequentially, in a series of runs, the observed Fisher information from previous runs is known. In the current work, two adaptive designs are proposed that use the observed Fisher information from previous runs to inform the design of future runs.