This paper is a broad and accessible survey of the methods we have at our disposal for
Monte Carlo gradient estimation in machine learning and across the statistical sciences: the …

This chapter reviews simulation-based methods for estimating gradients, which are central
to gradient-based simulation optimization algorithms such as stochastic approximation and …

Automatic differentiation (AD), a technique for constructing new programs which compute
the derivative of an original program, has become ubiquitous throughout scientific …

This contribution summarizes the design and analysis of experiments with computerized
simulation models. It focuses on two metamodel (surrogate, emulator) types, namely first …

In simulation of complex stochastic systems, such as Discrete-Event Systems (DES),
statistical distributions are used to model the underlying randomness in the system. A …

We analyze the variance of single-run unbiased stochastic derivative estimators. The
distribution of a specific conditional expectation characterizes an intrinsic distributional …

Quantiles play an important role in modelling quality of service in the service industry and in
modelling risk in the financial industry. The recent discovery that efficient simulation-based …

In this paper, we study cost functions over a finite collection of random variables. For these
types of models, a calculus of differentiation is developed that allows us to obtain a closed …

In this paper, we compare the infinitesimal perturbation analysis (IPA) and likelihood ratio
(LR) derivative estimators for the steady-state system time of the M/M/1 queue. We derive the …

This advanced tutorial aims at an exposition of problems in finance that are worthy of study
by the Monte Carlo research community. It describes problems in valuing and hedging …