Nested Gaussian process modeling and imputation of high-dimensional incomplete data under uncertainty-Reference-Cited by-同舟云学术

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Nested Gaussian process modeling and imputation of high-dimensional incomplete data under uncertainty

Published:2019-04-09 Issue:4 Volume:9 Page:315-326
ISSN:2472-5579
Container-title:IISE Transactions on Healthcare Systems Engineering
language:en
Short-container-title:IISE Transactions on Healthcare Systems Engineering

Author:

Imani Farhad¹,Cheng Changqing²,Chen Ruimin¹,Yang Hui¹

Affiliation:

1. Harold and Inge Marcus Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, University Park, PA, USA;

2. Department of Systems Science and Industrial Engineering, State University of New York, Binghamton, NY, USA

Funder

National Science Foundation

Publisher

Informa UK Limited

Subject

Public Health, Environmental and Occupational Health,Safety Research,Safety, Risk, Reliability and Quality

Link

https://www.tandfonline.com/doi/pdf/10.1080/24725579.2019.1583704

Reference33 articles.

1. Heterogeneous Sensing and Predictive Modeling of Postoperative Outcomes

2. A Novel Information-Theoretic Approach for Variable Clustering and Predictive Modeling Using Dirichlet Process Mixtures

3. Sparse Modeling and Recursive Prediction of Space–Time Dynamics in Stochastic Sensor Networks

4. Multi-scale Gaussian process experts for dynamic evolution prediction of complex systems

5. Time series forecasting for nonlinear and non-stationary processes: a review and comparative study

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