Master index Index for polypedal/gpml

# Index for polypedal/gpml

## Matlab files in this directory:

 Contents gpml: code from Rasmussen & Williams: Gaussian Processes for Machine Learning approxEP Expectation Propagation approximation to the posterior Gaussian Process. approxLA Laplace approximation to the posterior Gaussian Process. approximations approximations: Exact inference for Gaussian process classification is binaryEPGP binaryEPGP - The Expectation Propagation approximation for binary Gaussian binaryGP Approximate binary Gaussian Process classification. Two modes are possible: binaryLaplaceGP binaryLaplaceGP - Laplace's approximation for binary Gaussian process covConst covariance function for a constant function. The covariance function is covFunctions covariance functions to be use by Gaussian process functions. There are two covLINard Linear covariance function with Automatic Relevance Determination (ARD). The covLINone Linear covariance function with a single hyperparameter. The covariance covMatern3iso Matern covariance function with nu = 3/2 and isotropic distance measure. The covMatern5iso Matern covariance function with nu = 5/2 and isotropic distance measure. The covNNone Neural network covariance function with a single parameter for the distance covNoise Independent covariance function, ie "white noise", with specified variance. covPeriodic covariance function for a smooth periodic function, with unit period. The covProd covProd - compose a covariance function as the product of other covariance covRQard Rational Quadratic covariance function with Automatic Relevance Determination covRQiso Rational Quadratic covariance function with isotropic distance measure. The covSEard Squared Exponential covariance function with Automatic Relevance Detemination covSEiso Squared Exponential covariance function with isotropic distance measure. The covSum covSum - compose a covariance function as the sum of other covariance covTPeriodic covariance function for a smooth periodic function with specified period. cumGauss cumGauss - Cumulative Gaussian likelihood function. The expression for the gauher compute abscissas and weight factors for Gaussian-Hermite quadrature gpr gpr - Gaussian process regression, with a named covariance function. Two gprSRPP gprSRPP - Carries out approximate Gaussian process regression prediction likelihoods likelihood: likelihood functions are provided to be used by the binaryGP logistic logistic - logistic likelihood function. The expression for the likelihood is minimize Minimize a differentiable multivariate function. negLogML Compute the negative log marginal likelihood that the hyperparameters solve_chol solve_chol - solve linear equations from the Cholesky factorization. sq_dist sq_dist - a function to compute a matrix of all pairwise squared distances

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