Active Sampling: A Machine-Learning-Assisted Framework for Finite Population Inference with Optimal Subsamples

Data subsampling has become widely recognized as a tool to overcome computational and economic bottlenecks in analyzing massive datasets. We contribute to the development of adaptive design for estimation of finite population characteristics, using active learning and adaptive importance sampling. W...

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Bibliographic Details
Published in:	Technometrics Vol. 67; no. 1; pp. 46 - 57
Main Authors:	Imberg, Henrik, Yang, Xiaomi, Flannagan, Carol, Bärgman, Jonas
Format:	Journal Article
Language:	English
Published:	Alexandria Taylor & Francis 02.01.2025 American Society for Quality
Subjects:	Active learning Adaptive importance sampling Adaptive sampling Adaptiveimportance sampling Advanced driver assistance systems Bayesian analysis Computer and Information Sciences Computer simulation experiments Computer simulationexperiments Data collection Data- och informationsvetenskap (Datateknik) Estimating techniques Importance sampling Inverse probability weighting Inverseprobability weighting Machine learning Massive data points Optimal design Sampling methods Simulation Software Traffic safety assessment Traffic safetyassessment
ISSN:	0040-1706, 1537-2723, 1537-2723
Online Access:	Get full text
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