Gaussian Process Computations

The main interface to the celerite solver is through the GP class. This main purpose of this class is to exposes methods for efficiently computing the marginalized likelihood of a Gaussian Process (GP) model. The covariance matrix for the GP will be specified by a kernel function as described in the Kernel Building section.

The GP class implements the Python: Modeling with submodels called kernel and mean so the modeling language can be used to fit for parameters for each of these models.

Below, the methods of the GP object are described in detail but the most important methods to look at are probably GP.compute(), GP.log_likelihood(), and GP.predict().

class celerite.GP(kernel, mean=0.0, fit_mean=False, log_white_noise=None, fit_white_noise=False)

The main interface to the celerite Gaussian Process solver

Parameters

  • kernel – An instance of a subclass of terms.Term.

  • mean (Optional) – A simple mean value for the process. This can either be a float or a subclass of modeling.Model. (default: 0.0)

  • fit_mean (optional) – If False, all of the parameters of mean will be frozen. Otherwise, the parameter states are unaffected. (default: False)

apply_inverse(y)

Apply the inverse of the covariance matrix to a vector or matrix

Solve K.x = y for x where K is the covariance matrix of the GP with the white noise and yerr components included on the diagonal.

Parameters

  • y (array[n] or array[n, nrhs]) – The vector or matrix y

  • above. (described) –

Returns

The solution to the linear system. This will have the same shape as y.

Return type

array[n] or array[n, nrhs]

Raises

ValueError – For mismatched dimensions.

compute(t, yerr=1.123e-12, check_sorted=True, A=None, U=None, V=None)

Compute the extended form of the covariance matrix and factorize

Parameters

  • x (array[n]) – The independent coordinates of the data points. This array must be _sorted_ in ascending order.

  • yerr (Optional[float or array[n]]) – The measurement uncertainties for the data points at coordinates x. These values will be added in quadrature to the diagonal of the covariance matrix. (default: 1.123e-12)

  • check_sorted (bool) – If True, x will be checked to make sure that it is properly sorted. If False, the coordinates will be assumed to be in the correct order.

Raises

  • ValueError – For un-sorted data or mismatched dimensions.

  • solver.LinAlgError – For non-positive definite matrices.

dot(y, t=None, A=None, U=None, V=None, kernel=None, check_sorted=True)

Dot the covariance matrix into a vector or matrix

Compute K.y where K is the covariance matrix of the GP without the white noise or yerr values on the diagonal.

Parameters

  • y (array[n] or array[n, nrhs]) – The vector or matrix y described above.

  • kernel (Optional[terms.Term]) – A different kernel can optionally be provided to compute the matrix K from a different kernel than the kernel property on this object.

Returns

The dot product K.y as described above. This will have the same shape as y.

Return type

array[n] or array[n, nrhs]

Raises

ValueError – For mismatched dimensions.

get_matrix(x1=None, x2=None, include_diagonal=None, include_general=None)

Get the covariance matrix at given independent coordinates

Parameters

  • x1 (Optional[array[n1]]) – The first set of independent coordinates. If this is omitted, x1 will be assumed to be equal to x from a previous call to GP.compute().

  • x2 (Optional[array[n2]]) – The second set of independent coordinates. If this is omitted, x2 will be assumed to be x1.

  • include_diagonal (Optional[bool]) – Should the white noise and yerr terms be included on the diagonal? (default: False)

grad_log_likelihood(y, quiet=False)

Compute the gradient of the marginalized likelihood

The factorized matrix from the previous call to GP.compute() is used so compute must be called first. The gradient is taken with respect to the parameters returned by GP.get_parameter_vector(). This function requires the autograd package.

Parameters

  • y (array[n]) – The observations at coordinates x from GP.compute().

  • quiet (bool) – If true, return -numpy.inf and a gradient vector of zeros for non-positive definite matrices instead of throwing an error.

Returns

The gradient of marginalized likelihood with respect to the parameter vector.

Raises

  • ValueError – For mismatched dimensions.

  • solver.LinAlgError – For non-positive definite matrices.

log_likelihood(y, _const=1.8378770664093453, quiet=False)

Compute the marginalized likelihood of the GP model

The factorized matrix from the previous call to GP.compute() is used so compute must be called first.

Parameters

  • y (array[n]) – The observations at coordinates x from GP.compute().

  • quiet (bool) – If true, return -numpy.inf for non-positive definite matrices instead of throwing an error.

Returns

The marginalized likelihood of the GP model.

Return type

float

Raises

  • ValueError – For mismatched dimensions.

  • solver.LinAlgError – For non-positive definite matrices.

property mean

The mean modeling.Model

predict(y, t=None, return_cov=True, return_var=False)

Compute the conditional predictive distribution of the model

You must call GP.compute() before this method.

Parameters

  • y (array[n]) – The observations at coordinates x from GP.compute().

  • t (Optional[array[ntest]]) – The independent coordinates where the prediction should be made. If this is omitted the coordinates will be assumed to be x from GP.compute() and an efficient method will be used to compute the prediction.

  • return_cov (Optional[bool]) – If True, the full covariance matrix is computed and returned. Otherwise, only the mean prediction is computed. (default: True)

  • return_var (Optional[bool]) – If True, only return the diagonal of the predictive covariance; this will be faster to compute than the full covariance matrix. This overrides return_cov so, if both are set to True, only the diagonal is computed. (default: False)

Returns

mu, (mu, cov), or (mu, var) depending on the values of return_cov and return_var. These output values are: (a) mu (ntest,): mean of the predictive distribution, (b) cov (ntest, ntest): the predictive covariance matrix, and (c) var (ntest,): the diagonal elements of cov.

Raises

ValueError – For mismatched dimensions.

sample(size=None)

Sample from the prior distribution over datasets

Parameters

size (Optional[int]) – The number of samples to draw.

Returns

The samples from the prior distribution over datasets.

Return type

array[n] or array[size, n]

sample_conditional(y, t=None, size=None, regularize=None)

Sample from the conditional (predictive) distribution

Note: this method scales as O(M^3) for large M, where M == len(t).

Parameters

  • y (array[n]) – The observations at coordinates x from GP.compute().

  • t (Optional[array[ntest]]) – The independent coordinates where the prediction should be made. If this is omitted the coordinates will be assumed to be x from GP.compute() and an efficient method will be used to compute the prediction.

  • size (Optional[int]) – The number of samples to draw.

  • regularize (Optional[float]) – For poorly conditioned systems, you can provide a small number here to regularize the predictive covariance. This number will be added to the diagonal.

Returns

The samples from the conditional distribution over datasets.

Return type

array[n] or array[size, n]