Selection of the most probable best under input uncertainty

[2207.07533] Selection of the Most Probable Best - arXiv

We define the most probable best (MPB) to be the solution that has the largest probability of being optimal with respect to the distribution.

Selection of the Most Probable Best Under Input Uncertainty

We consider a ranking and selection problem whose configuration depends on a common input model estimated from finite real-world observations.

Selection of the most probable best under input uncertainty

Taking the Bayesian view, the most probable best is defined as the solution whose posterior probability of being the best is the largest given the real-world ...

[PDF] Selection of the Most Probable Best | Semantic Scholar

38 References · Selection of the Most Probable Best Under Input Uncertainty · Optimal Selection of the Most Probable Multinomial Alternative · Robust ranking and ...

(PDF) Selection of the Most Probable Best - ResearchGate

Given that the uncertainty of the input model is captured by a probability simplex on a finite support, we define the most probable best (MPB) ...

Eunhye Song - Google Sites

Kyoung-Kuk Kim, Taeho Kim*, Eunhye Song (2021) Selection of the Most Probable Best under Input Uncertainty, In Proceedings of the 2021 Winter ...

Optimizing Input Data Acquisition for Ranking and Selection

To optimize input data acquisition, we first show that the most probable best (MPB)―the solution with the largest posterior probability of being optimal ( ...

Optimizing Input Data Acquisition for Ranking and Selection

This paper concerns a Bayesian ranking and selection (R&S) problem under input uncertainty when all solutions are simulated with common input models ...

Ranking and Selection under Input Uncertainty: A Budget Allocation ...

Hi(θc), which means that the optimal system is perturbed, and the more we simulate, the less likely we will select the true best system b. Therefore, input ...

Ranking and selection under input uncertainty

Hi(θc), which means that the optimal system is perturbed, and the more we simulate, the less likely we will select the true best system b. Therefore, input ...

data-driven ranking and selection under input uncertainty - arXiv

If input data is given as a static dataset which does not grow over time, then our best hope is to gauge the impact of IU and provide a more ...

ADVANCED TUTORIAL: INPUT UNCERTAINTY QUANTIFICATION

Under some regularity conditions, the posterior distribution of Θ converges to a degenerate distribution at θc independent of the choice of the prior ...

Optimal Computing Budget Allocation for Data-Driven Ranking and ...

Back to Top. Next. Figures; References; Related; Information. Recommended. Data-Driven Ranking and Selection Under Input Uncertainty · Di Wu. Di ...

Stochastic simulation under input uncertainty: A Review

Simulation is an invaluable tool for practitioners for the analysis of complex systems and processes. It is the method of choice when real-world experiments on ...

A Most Probable Point-Based Method for Efficient Uncertainty Analysis

Methods used in this context are based on the point with largest probability density (typically of a Gaussian distribution), combined with set approximations ...

Robust measurement selection design for experimental systems ...

In most cases, explicit forms of input uncertainty projected to outputs and further to parameter estimations are hardly obtainable. The. Monte-Carlo method can ...

[PDF] Data-Driven Ranking and Selection Under Input Uncertainty

... best one with a high confidence. In “Data-Driven Ranking and Selection Under Input Uncertainty,” Wu, Wang, and Zhou consider such a simulation-based ranking ...

Efficient uncertainty propagation for parameterized p-box using ...

These input uncertainties may be characterized as either aleatory uncertainties, which are irreducible variabilities inherent in nature, or epistemic ...

Uncertainty analysis of model inputs in riverine water temperature ...

Thus, it is important to select the most efficient method to assess the uncertainties associated with the input data and model parameters, as ...

Uncertainty quantification - Wikipedia

Uncertainty quantification (UQ) is the science of quantitative characterization and estimation of uncertainties in both computational and real world ...