A Guide to the GauPro R package1 years ago
Overview | Installation | Example in 1-Dimension | Factor data: fitting the diamonds dataset | Constructing a kernel | Using kernels | Combining kernels | Intro to GPs | Trends
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Spatial derivatives of Gaussian process models2 years ago
Gradient of mean function | Hessian of mean function | Gaussian correlation derivative | Gradient distribution | Gradient expected value | Variance of the gradient | Distribution of the gradient norm squared | Mean of gradient norm squared | Full distribution of gradient norm squared | General derivation | Relating this back to $||g||^2$ | References
Introduction to Gaussian Processes2 years ago
Mean function $\mu$ | Covariance function $\Sigma$ | Gaussian correlation | Likelihood function and parameter estimation | Estimate for constant $\mu$ | Estimate for $\sigma$ | Prediction of new points | Conditional distribution | Predicting
Derivatives for estimating Gaussian process parameters2 years ago
Deviance | Nugget | Gaussian correlation | Lifted brownian covariance | Likelihood
An Introduction to TestFunctions2 years ago
How do I use this package? | General function information | Random wave functions | Function enhancers | Additional information
An Introduction to the CGGP Package7 years ago
Introduction | How to use CGGP | Plotting CGGP objects
Introduction to the ContourFunctions R package7 years ago
cf_grid | cf_func | cf_data | afterplotfunc | cf | cf_highdim | cf_4dim | Making plots with ggplot2 | Adding contour lines to plots
Leave-one-out cross-validation and error correction9 years ago
Leave-one-out predictions using Gaussian processes | Getting the inverse of a submatrix | Leave-one-out covariance matrix inverse for Gaussian processes | Leave-one-out prediction
