Dynamic Sampling Allocation and Design Selection

2016 ◽  
Vol 28 (2) ◽  
pp. 195-208 ◽  
Author(s):  
Yijie Peng ◽  
Chun-Hung Chen ◽  
Michael C. Fu ◽  
Jian-Qiang Hu
Keyword(s):  
Design Selection ◽  
Dynamic Sampling ◽  
Sampling Allocation

Dynamic Sampling Allocation Under Finite Simulation Budget for Feasibility Determination

2021 ◽  
Author(s):  
Zhongshun Shi ◽  
Yijie Peng ◽  
Leyuan Shi ◽  
Chun-Hung Chen ◽  
Michael C. Fu
Keyword(s):  
Stochastic Systems ◽  
Asymptotic Optimality ◽  
Asymptotic Properties ◽  
Sampling Procedure ◽  
Allocation Rule ◽  
Finite Sample ◽  
Significant Difference ◽  
Finite Set ◽  
Dynamic Sampling ◽  
Sampling Allocation

Monte Carlo simulation is a commonly used tool for evaluating the performance of complex stochastic systems. In practice, simulation can be expensive, especially when comparing a large number of alternatives, thus motivating the need to intelligently allocate simulation replications. Given a finite set of alternatives whose means are estimated via simulation, we consider the problem of determining the subset of alternatives that have means smaller than a fixed threshold. A dynamic sampling procedure that possesses not only asymptotic optimality, but also desirable finite-sample properties is proposed. Theoretical results show that there is a significant difference between finite-sample optimality and asymptotic optimality. Numerical experiments substantiate the effectiveness of the new method. Summary of Contribution: Simulation is an important tool to estimate the performance of complex stochastic systems. We consider a feasibility determination problem of identifying all those among a finite set of alternatives with mean smaller than a given threshold, in which the means are unknown but can be estimated by sampling replications via stochastic simulation. This problem appears widely in many applications, including call center design and hospital resource allocation. Our work considers how to intelligently allocate simulation replications to different alternatives for efficiently finding the feasible alternatives. Previous work focuses on the asymptotic properties of the sampling allocation procedures, whereas our contribution lies in developing a finite-budget allocation rule that possesses both asymptotic optimality and desirable finite-budget properties.

Efficient Sampling Allocation Procedures for Optimal Quantile Selection

2020 ◽  
Author(s):  
Yijie Peng ◽  
Chun-Hung Chen ◽  
Michael C. Fu ◽  
Jian-Qiang Hu ◽  
Ilya O. Ryzhov
Keyword(s):  
Sample Size ◽  
Numerical Experiments ◽  
Ranking And Selection ◽  
Computationally Efficient ◽  
Selection Procedures ◽  
Efficient Sampling ◽  
Selection For ◽  
Dynamic Sampling ◽  
Efficient Selection ◽  
Sampling Allocation

We propose a dynamic sampling allocation and selection paradigm for finding the alternative with the optimal quantile in a Bayesian framework. Myopic allocation policies (MAPs), analogous to existing methods in classic ranking and selection for selecting the alternative with the optimal mean, and computationally efficient selection policies are derived for selecting the alternative with the optimal quantile. Under certain conditions, we prove that the proposed MAPs and selection procedures are consistent, which means that the best quantile would be eventually correctly selected as the sample size goes to infinity. Numerical experiments demonstrate that the proposed schemes can significantly improve the performance.

A Supervised Learning Approach for Dynamic Sampling (SLADS) in Raman Hyperspectral Imaging

2018 ◽  
Vol 2018 (15) ◽  
pp. 132-1-1323
Author(s):  
Shijie Zhang ◽  
Zhengtian Song ◽  
G. M. Dilshan P. Godaliyadda ◽  
Dong Hye Ye ◽  
Atanu Sengupta ◽  
...  
Keyword(s):  
Supervised Learning ◽  
Hyperspectral Imaging ◽  
Learning Approach ◽  
Dynamic Sampling

The visual arrays task…visual storage capacity or attention control?

2019 ◽  
Author(s):  
Jessie Martin ◽  
Jason S. Tsukahara ◽  
Christopher Draheim ◽  
Zach Shipstead ◽  
Cody Mashburn ◽  
...  
Keyword(s):  
Working Memory ◽  
Storage Capacity ◽  
Working Memory Capacity ◽  
Memory Capacity ◽  
Attention Control ◽  
Data Sets ◽  
Independent Data ◽  
Visual Storage ◽  
Design Selection ◽  
The Relationship

**The uploaded manuscript is still in preparation** In this study, we tested the relationship between visual arrays tasks and working memory capacity and attention control. Specifically, we tested whether task design (selection or non-selection demands) impacted the relationship between visual arrays measures and constructs of working memory capacity and attention control. Using analyses from 4 independent data sets we showed that the degree to which visual arrays measures rely on selection influences the degree to which they reflect domain-general attention control.

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