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pro vyhledávání: '"Shashaani, Sara"'
Model update (MU) and candidate evaluation (CE) are classical steps incorporated inside many stochastic trust-region (TR) algorithms. The sampling effort exerted within these steps, often decided with the aim of controlling model error, largely deter
Externí odkaz:
http://arxiv.org/abs/2405.20116
Decision trees built with data remain in widespread use for nonparametric prediction. Predicting probability distributions is preferred over point predictions when uncertainty plays a prominent role in analysis and decision-making. We study modifying
Externí odkaz:
http://arxiv.org/abs/2402.11052
Calibrating simulation models that take large quantities of multi-dimensional data as input is a hard simulation optimization problem. Existing adaptive sampling strategies offer a methodological solution. However, they may not sufficiently reduce th
Externí odkaz:
http://arxiv.org/abs/2401.14558
We present batching as an omnibus device for uncertainty quantification using simulation output. We consider the classical context of a simulationist performing uncertainty quantification on an estimator $\theta_n$ (of an unknown fixed quantity $\the
Externí odkaz:
http://arxiv.org/abs/2311.04159
Autor:
Ha, Yunsoo, Shashaani, Sara
Adaptive sampling with interpolation-based trust regions or ASTRO-DF is a successful algorithm for stochastic derivative-free optimization with an easy-to-understand-and-implement concept that guarantees almost sure convergence to a first-order criti
Externí odkaz:
http://arxiv.org/abs/2305.10650
Autor:
Vahdat, Kimia, Shashaani, Sara
In predictive modeling with simulation or machine learning, it is critical to accurately assess the quality of estimated values through output analysis. In recent decades output analysis has become enriched with methods that quantify the impact of in
Externí odkaz:
http://arxiv.org/abs/2207.13612
Publikováno v:
In Applied Energy 1 October 2023 347
Publikováno v:
ACM Transactions on Modeling & Computer Simulation; Oct2024, Vol. 34 Issue 4, p1-29, 29p
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