BQStandardBeliefUpdate¶
- class probnum.quad.solvers.belief_updates.BQStandardBeliefUpdate(jitter, scale_estimation)¶
Bases:
BQBeliefUpdate
Updates integral belief and state using standard Bayesian quadrature based on standard Gaussian process inference.
- Parameters
jitter (FloatLike) – Non-negative jitter to numerically stabilise kernel matrix inversion.
scale_estimation (Optional[str]) – Estimation method to use to compute the scale parameter.
Methods Summary
__call__
(bq_state, new_nodes, new_fun_evals, ...)Updates integral belief and BQ state according to the new data given.
compute_gram_cho_factor
(gram)Compute the Cholesky decomposition of a positive-definite Gram matrix for use in scipy.linalg.cho_solve
gram_cho_solve
(gram_cho_factor, z)Wrapper for scipy.linalg.cho_solve.
predict_integrand
(x, bq_state)Predictive mean and variances of the integrand at given nodes.
Methods Documentation
- __call__(bq_state, new_nodes, new_fun_evals, *args, **kwargs)[source]¶
Updates integral belief and BQ state according to the new data given.
- Parameters
- Returns
updated_belief – Gaussian integral belief after conditioning on the new nodes and evaluations.
updated_state – Updated version of
bq_state
that contains all updated quantities.
- Return type
- compute_gram_cho_factor(gram)¶
Compute the Cholesky decomposition of a positive-definite Gram matrix for use in scipy.linalg.cho_solve
Warning
Uses scipy.linalg.cho_factor. The returned matrix is only to be used in scipy.linalg.cho_solve.
- Parameters
gram (ndarray) – symmetric pos. def. kernel Gram matrix \(K\), shape (nevals, nevals)
- Returns
The upper triangular Cholesky decomposition of the Gram matrix. Other parts of the matrix contain random data. A boolean that indicates whether the matrix is lower triangular (always False but needed for scipy).
- Return type
gram_cho_factor
- static gram_cho_solve(gram_cho_factor, z)¶
Wrapper for scipy.linalg.cho_solve. Meant to be used for linear systems of the gram matrix. Requires the solution of scipy.linalg.cho_factor as input.