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On the inference of applying Gaussian process modeling to a deterministic function
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Smoothing Spline ANOVA Models
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The Theory of Stochastic Processes III
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Convex Functions and Their Applications
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Convergence Types and Rates in Generic Karhunen-Loève Expansions with Applications to Sample Path Properties
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Asymptotic properties of a maximum likelihood estimator with data from a Gaussian process
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Uncertainty quantification in multiscale simulation of woven fiber composites
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Cross Validation and Maximum Likelihood estimations of hyper-parameters of Gaussian processes with model misspecification
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Closed form representations and properties of the generalised Wendland functions
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Credible sets in the fixed design model with Brownian motion prior
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Error estimates for radial basis function interpolation
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Function Spaces, Entropy Numbers, Differential Operators
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Interpolation of spatial data – A stochastic or a deterministic problem?
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An Alternative Measure of the Reliability of Ordinary Kriging Estimates
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Cost-Effectiveness of Hypertension Therapy According to 2014 Guidelines
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Controlling Sources of Inaccuracy in Stochastic Kriging
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Bayesian Confidence Intervals for Smoothing Splines
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Fixed-Domain Asymptotics for Spatial Periodograms
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Multiresolution Functional ANOVA for Large-Scale, Many-Input Computer Experiments
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On Prediction Properties of Kriging: Uniform Error Bounds and Robustness
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On the Choice of Nugget in Kriging Modeling for Deterministic Computer Experiments
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Local Gaussian Process Approximation for Large Computer Experiments
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Bounds for the Determinant of the Sum of Hermitian Matrices
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Modeling the Sexual Transmissibility of Human Papillomavirus Infection using Stochastic Computer Simulation and Empirical Data from a Cohort Study of Young Women in Montreal, Canada
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Maximum Likelihood Estimation of Models for Residual Covariance in Spatial Regression
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Local error estimates for radial basis function interpolation of scattered data
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Spectral Theory and Differential Operators
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The parameter space of galaxy formation
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Random Number Generation and Quasi-Monte Carlo Methods
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A Theoretical Framework for Calibration in Computer Models: Parametrization, Estimation and Convergence Properties
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Maximum Likelihood Estimation for a Smooth Gaussian Random Field Model
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Maximum Likelihood Estimation and Uncertainty Quantification for Gaussian Process Approximation of Deterministic Functions
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Regularization Theory and Neural Networks Architectures
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Analysis of Computer Experiments Using Penalized Likelihood in Gaussian Kriging Models
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Nonseparable, Stationary Covariance Functions for Space–Time Data
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Inconsistent Estimation and Asymptotically Equal Interpolations in Model-Based Geostatistics
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Random Number Generation and Quasi-Monte Carlo Methods.
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Interpolation of Spatial Data: Some Theory for Kriging
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Asymptotic analysis of covariance parameter estimation for Gaussian processes in the misspecified case
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Penalized blind kriging in computer experiments
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Gaussian Processes for Machine Learning
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Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Statistical learning and causal inference for energy production
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