Master index | Index for polypedal/gpml |

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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