gp | R Documentation |

Define Gaussian processes, and project them to new coordinates.

```
gp(x, kernel, inducing = NULL, n = 1, tol = 1e-04)
project(f, x_new, kernel = NULL)
```

`x, x_new` |
greta array giving the coordinates at which to evaluate the Gaussian process |

`kernel` |
a kernel function created using one of the kernel() methods |

`inducing` |
an optional greta array giving the coordinates of inducing points in a sparse (reduced rank) Gaussian process model |

`n` |
the number of independent Gaussian processes to define with the same kernel |

`tol` |
a numerical tolerance parameter, added to the diagonal of the self-covariance matrix when computing the cholesky decomposition. If the sampler is hitting a lot of numerical errors, increasing this parameter could help |

`f` |
a greta array created with |

`gp()`

returns a greta array representing the values of the
Gaussian process(es) evaluated at `x`

. This Gaussian process can be
made sparse (via a reduced-rank representation of the covariance) by
providing an additional set of inducing point coordinates `inducing`

.
`project()`

evaluates the values of an existing Gaussian process
(created with `gp()`

) to new data.

A greta array

```
## Not run:
# build a kernel function on two dimensions
k1 <- rbf(lengthscales = c(0.1, 0.2), variance = 0.6)
k2 <- bias(variance = lognormal(0, 1))
K <- k1 + k2
# use this kernel in a full-rank Gaussian process
f <- gp(1:10, K)
# or in sparse Gaussian process
f_sparse <- gp(1:10, K, inducing = c(2, 5, 8))
# project the values of the GP to new coordinates
f_new <- project(f, 11:15)
# or project with a different kernel (e.g. a sub-kernel)
f_new_bias <- project(f, 11:15, k2)
## End(Not run)
```

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