This paper surveys the use of Monte Carlo sampling-based methods for stochastic
optimization problems. Such methods are required when—as it often happens in practice …

This paper considers the optimal adaptive allocation of measurement effort for identifying the
best among a finite set of options or designs. An experimenter sequentially chooses designs …

This chapter reviews the problem of selecting the best of a finite set of alternatives, where
best is defined with respect to the highest mean performance, and where the performance is …

Firms using online advertising regularly run experiments with multiple versions of their ads
since they are uncertain about which ones are most effective. During a campaign, firms try to …

We explore a new model of bandit experiments where a potentially nonstationary sequence
of contexts influences arms' performance. Context-unaware algorithms risk confounding …

Fully sequential ranking-and-selection (R&S) procedures to find the best from a finite set of
simulated alternatives are often designed to be implemented on a single processor …

We consider parallel global optimization of derivative-free expensive-to-evaluate functions,
and propose an efficient method based on stochastic approximation for implementing a …

Under a Bayesian framework, we formulate the fully sequential sampling and selection
decision in statistical ranking and selection as a stochastic control problem, and derive the …

Consumers often learn the weights they ascribe to product attributes (“preference weights”)
as they search. For example, after test driving cars, a consumer might find that he or she …

Many procedures have been proposed in the literature to select the simulated alternative
with the best mean performance from a finite set of alternatives. Among these procedures …