New Two-Stage and Sequential Procedures for Selecting the Best Simulated System

By S. Chick, Koichiro Inoue. Operational Research, 2001

377 citations recorded by the source corpus.

Subfields: Emergency Medical Services, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/a67f8cbe851f99051f213ffed79474e29acf9978

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In Emergency Medical Services

In Management Science and Operations Research

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Citations in the corpus

Simulation optimization: a review, new developments, and applications (2005, background): “Furthermore, in the stochastic setting, the estimation problem must also be incorporated into the algorithm.”
The Knowledge-Gradient Policy for Correlated Normal Beliefs (2009, methodology): “Several approximate sequential and two-stage policies exist for maximizing a quality measure applied to the chosen alternative, beginning with Gupta and Miescke (1996), and continuing with two distinct families of policies: the optimal computing budget allocation (OCBA) (Chen et al. 1996, 2000; He e”
Proceedings of the 2008 winter simulation conference (2008, neutral): “Gupta et al. (1973) propose several such scalar functions.”
Selecting the best system when systems are revealed sequentially (2007, background): “These procedures require that all alternative systems are known at the beginning of the experiment.”
Fully sequential procedures for comparing constrained systems via simulation (2010, methodology): “[7], Chick [8], and Chick and Inoue [9, 10] all developed procedures that are a great deal more efficient than classical R&S procedures such as that due to Rinott [26].”
A fully sequential procedure for indifference-zone selection in simulation (2001, background): “Chick and Inoue (1999, 2000) also present procedures that seek to effi-”
A Knowledge-Gradient Policy for Sequential Information Collection (2008, methodology): “The OCBA policy in [18] does not assume, as the other OCBA approaches do, that the posterior predictive mean is equal to the prior predictive mean, and in this regard it is more similar to the approach of [12] discussed below.”
A Tutorial on Bayesian Optimization (2018, methodology): “This algorithm was ﬁrst proposed in Frazier et al. (2009) for GP regression over discrete A , building on earlier work (Frazier et al., 2008) that proposed the same algorithm for Bayesian ranking and selection (Chick and Inoue, 2001) with an independent prior.”
Monte Carlo sampling-based methods for stochastic optimization (2014, background): “Glynn and Juneja [90], instead, use large deviation results to determine asymptotically optimal sample size allocations to maximize the decay rate of 1−PCS. Chick and Inoue [48], on the other hand, maximize the expected value of information.”
Simple Bayesian Algorithms for Best Arm Identification (2016, background): “Optimal budget allocations have been extended in various directions, for example to address Bayesian expected loss objectives [Chick and Inoue, 2001], the problem of identifying an optimum subject to stochastic constraints [Hunter and Pasupathy, 2013], and the problem of identifying the top m altern”