[2207.07533] Selection of the Most Probable Best

[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 and ...

Selection of the Most Probable Best - arXiv

We consider an expected-value ranking and selection (R&S) problem where all k solutions' simulation outputs depend on a common parameter whose ...

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

The most probable best (MPB) is defined to be the solution that has the largest probability of being optimal with respect to the distribution and an ...

(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) ...

arXiv:2207.07533v1 [stat.ME] 15 Jul 2022 - ResearchGate

... Selection of the most probable best under input uncertainty. Kim S, Feng B,. Smith K, Masoud S, Zheng Z, eds., Proceedings of the 2021 Winter Simulation ...

Selection of the Most Probable Best | AI Research Paper Details

We consider an expected-value ranking and selection (R&S) problem where all k solutions' simulation outputs depend on a common parameter ...

Selection of the Most Probable Best - Instant Read & Key Insights

The most probable best (MPB) is the solution that has the highest probability of being the best under the uncertainty in the input model parameters.

Optimizing Input Data Acquisition for Ranking and Selection

“Selection of the Most Probable Best”. https://arxiv.org/abs/2207.07533. Liu, T., Y. Lin, and E. Zhou. 2021. “A Bayesian Approach to Online Simulation ...

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 ...

Eunhye Song - DBLP

Selection of the Most Probable Best. CoRR abs/2207.07533 (2022); 2021 ... Selection of the Most Probable Best Under Input Uncertainty.

risk-sensitive ordinal optimization - Winter Simulation Conference

2023), and selection of the most probable best under input uncertainty (Kim et al. 2022). Recently, Chen and Ryzhov (2022) propose the balancing optimal large ...

A/B Tests Under a Safety Budget: A Simulation-Optimization Point of ...

Selection of the most probable best. arXiv preprint. arXiv:2207.07533, 2022. Ron Kohavi, Diane Tang, and Ya Xu. Trustworthy online controlled ...

0000-0002-5171-0614 - Eunhye Song - ORCID

Selection of the Most Probable Best. arXiv. 2022 | Other. DOI: 10.48550/arXiv.2207.07533. EID: 2-s2.0-85134758893. Part of ISSN: 23318422. Contributors: Kim, T ...

arxivsearch/variational/README.md at master - GitHub

Eunhye Song|[Selection of the Most Probable Best](http://arxiv.org/abs/2207.07533v1)|2022-07-15 15:27:27+00:00|15-07-2022. Behçet Açıkmeşe|[Set-based value ...

Selection of the Most Probable Best - Instant Read & Key ... - Linnk AI

입력 모델의 불확실성으로 인해 발생하는 시뮬레이션 출력의 불확실성을 고려하여 가장 가능성 높은 최적 솔루션을 효율적으로 찾는 방법을 제안한다.