Nonparametric spatial regression, emphasizing Gaussian processes in Chapter 5, benefits from a more agnostic approach to design compared to classical, linear modeling-based, response surface methods. This segment puts the cart before the horse a little. A Numerical Linear Algebra for Fast GPs.10.3.4 Optimization, level sets, calibration and more.10.3.1 Integrated mean-squared prediction error.10.1.2 Efficient inference and prediction under replication.10.1 Replication and stochastic kriging.9.3.4 Global/local multi-resolution effect.9.2.3 Regression tree extensions, off-shoots and fix-ups.9.2 Partition models and regression trees.9.1.3 Practical Bayesian inference and UQ.9.1.2 Sharing load between mean and variance.8.2.3 First-order and total sensitivity.7.3.5 Augmented Lagrangian Bayesian optimization (ALBO).7.2.5 Illustrating conditional improvement and noise.6.2.2 A more aggregate criteria: active learning Cohn.6.2.1 Whack-a-mole: active learning MacKay.6.1.2 Minimizing predictive uncertainty.5.3 Some interpretation and perspective.5.2.4 Lengthscale: rate of decay of correlation.5.2.3 Derivative-based hyperparameter optimization.3.2.4 Confidence in the stationary point.2.1.2 Sequential design and nonstationary surrogate modeling.1.1.3 General models, inference and sequential design.
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