GeneralKernel.generate_noise

GeneralKernel.generate_noise#

GeneralKernel.generate_noise(hp: Any, x_l: Array, x_t: Array, size: int | None = 1, stored_values: Any | None = {}) → Array[source]#

Generate noise with the covariance matrix returned by this kernel using the input hyperparameters hp.

Parameters:

  • hp (Pytree) – Hyperparameters needed to build the covariance matrix K. Will be unaffected if additional mean function parameters are also included.

  • x_l (JAXArray) – Array containing wavelength/vertical dimension regression variable(s). May be of shape (N_l,) or (d_l,N_l) for d_l different wavelength/vertical regression variables.

  • x_t (JAXArray) – Array containing time/horizontal dimension regression variable(s). May be of shape (N_t,) or (d_t,N_t) for d_t different time/horizontal regression variables.

  • size (int, optional) – The number of different draws of noise to generate. Defaults to 1.

  • stored_values (PyTree) – Stored values from the decomposition of the covariance matrix. For GeneralKernel this consists of the Cholesky factor and the log determinant of K.

Returns:

Generate noise of shape (N_l, N_t) if size = 1 or (N_l, N_t, size) if size > 1.

Return type:

JAXArray

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