Adaptive designs for optimal observed Fisher information

Expected Fisher information can be founda prioriand 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 knowna 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.