Stochastic gradient estimation techniques are methodologies for deriving computationally efficient estimators used in simulation optimization and sensitivity analysis of complex stochastic systems that require simulation to estimate their performance. Using a simple illustrative example, the three most well-known direct techniques that lead to unbiased estimators are presented: perturbation analysis, the likelihood ratio (score function) method, and weak derivatives (also known as measure-valued differentiation).
A few real-world applications are discussed and then some recent research is summarized.