pymc.gp.Latent.prior#
- Latent.prior(name, X, n_outputs=1, reparameterize=True, jitter=1e-06, **kwargs)[source]#
Return the GP prior distribution evaluated over the input locations X.
This is the prior probability over the space of functions described by its mean and covariance function.
\[f \mid X \sim \text{MvNormal}\left( \mu(X), k(X, X') \right)\]- Parameters:
- name
str Name of the random variable
- Xarray_like
Function input values. If one-dimensional, must be a column vector with shape (n, 1).
- n_outputs
int, default 1 Number of output GPs. If you’re using dims, make sure their size is equal to (n_outputs, X.shape[0]), i.e the number of output GPs by the number of input points. Example: gp.prior(“f”, X=X, n_outputs=3, dims=(“n_gps”, “x_dim”)), where len(n_gps) = 3 and len(x_dim = X.shape[0].
- reparameterizebool, default
True Reparameterize the distribution by rotating the random variable by the Cholesky factor of the covariance matrix.
- jitter
float, default 1e-6 A small correction added to the diagonal of positive semi-definite covariance matrices to ensure numerical stability.
- **kwargs
Extra keyword arguments that are passed to
MvNormaldistribution constructor.
- name