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Brownian Integrated Covariance Functions for Gaussian Process Modeling: Sigmoidal Versus Localized Basis Functions
Ning Zhang,
Daniel W. Apley
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Corresponding author for this work
Industrial Engineering and Management Sciences
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peer-review
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Scopus citations
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Dive into the research topics of 'Brownian Integrated Covariance Functions for Gaussian Process Modeling: Sigmoidal Versus Localized Basis Functions'. Together they form a unique fingerprint.
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Keyphrases
Gaussian Process Model
100%
Covariance Function
100%
Integrated Covariance Matrix
100%
Localized Basis Function
100%
Response Surface
50%
Nonlocalized
33%
Fractional Brownian Field
33%
Modeling Data
16%
Kriging
16%
Kriging Model
16%
Popular
16%
Response Predictors
16%
Integrated Power
16%
Integral Representation
16%
Covariance Model
16%
Field Model
16%
Regular Powers
16%
Mathematics
Covariance Function
100%
Gaussian Process
100%
Basis Function
100%
Power Exponential
50%
Response Surface
50%
Kriging
33%
Integral Representation
16%
Gaussian Distribution
16%
Covariance Model
16%
Supplementary Material
16%
Agricultural and Biological Sciences
Covariance
100%
Kriging
28%