approx | R Documentation |

Functions for initializing the approximation for the latent values, which can
then be passed to `gp_init`

.
The supported methods are:

`approx_laplace`

Laplace's method, that is, based on local second order approximation to the log likelihood. For Gaussian likelihood, this means exact inference (no approximation).

`approx_ep`

Expectation propagation, EP. Approximates the likelihood by introducing Gaussian pseudo-data so that the posterior marginals match to the so called tilted distributions (leave-one-out posterior times the true likelihood factor) as closely as possible. Typically more accurate than Laplace, but slower.

approx_laplace(maxiter = 30, tol = 1e-04) approx_ep(damping = 0.9, quad_order = 11, maxiter = 100)

`maxiter` |
Maximum number of iterations in the Laplace/EP iteration. |

`tol` |
Convergence tolerance. |

`damping` |
Damping factor for EP. Should be between 0 and 1. Smaller values typically lead to more stable iterations, but also increase the number of iterations, and thus make the algorithm slower. |

`quad_order` |
Order of the Gauss-Hermite quadrature used to evaluate the required tilted moments in EP. |

The approximation object.

Rasmussen, C. E. and Williams, C. K. I. (2006). Gaussian processes for machine learning. MIT Press.

# Basic usage gp <- gp_init( cfs = cf_sexp(), lik = lik_bernoulli(), method = method_fitc(num_inducing = 100), approx = approx_ep() )

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