SharedIndependentKernel

class pmrf.covariance_kernels.SharedIndependentKernel(base_kernel: AbstractCovarianceKernel, output_shape: tuple)

Bases: AbstractCovarianceKernel

Evaluates a base kernel and broadcasts its output to represent multiple independent dimensions (e.g., real and imaginary parts) withed share hyperparameters.

Parameters:

  • base_kernel (CovarianceKernel) – The underlying kernel whose parameters are shared.

  • output_shape (tuple) – The shape of the independent outputs to broadcast to.

__call__(x1, x2, key=None)

Evaluate the kernel between two points.

Parameters:

  • x1 (jnp.ndarray) – First input point.

  • x2 (jnp.ndarray) – Second input point.

  • key (jax.random.PRNGKey, optional) – Random key for stochastic kernels.

Returns:

Kernel covariance scalar.

Return type:

jnp.ndarray

base_kernel: AbstractCovarianceKernel

The base kernel.

output_shape: tuple

The output shape.