 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 |
Master index