Optimal stopping rules for exponential data

Abstract

A problem of sequential sampling from an Exponential Distribution is considered in this research. The problem is formulated in the stochastic dynamic
programming framework and the objective is to determine a control policy maximizing the total expected reward. It is assumed that under standard
assumptions the control limit policy is optimal. Two types of optimal stopping problems are considered. First one is the problem of sampling without
recall that once the decision maker cannot return to that observation at a later time, the second type of optimal stopping problems is sampling with recall
where the decision maker can select any observation which he has taken earlier.