Quadrature / Numerical Integration of Functions.

This package implements Bayesian quadrature rules used for numerical integration of functions on a given domain. Such methods integrate a function by iteratively building a probabilistic model and adaptively choosing points to evaluate the integrand based on said model.

## Functions¶

 bayesquad(fun, input_dim[, kernel, domain, ...]) Infer the solution of the uni- or multivariate integral $$\int_\Omega f(x) d \mu(x)$$ on a hyper-rectangle $$\Omega = [a_1, b_1] \times \cdots \times [a_D, b_D]$$. bayesquad_from_data(nodes, fun_evals[, ...]) Infer the value of an integral from a given set of nodes and function evaluations.

## Classes¶

 BayesianQuadrature(kernel, measure, policy, ...) A base class for Bayesian quadrature. IntegrationMeasure(domain, input_dim) An abstract class for a measure against which a target function is integrated. Dummy stopping criterion that always stops. KernelEmbedding(kernel, measure) Integrals over kernels against integration measures. GaussianMeasure(mean, cov[, input_dim]) Gaussian measure on Euclidean space with given mean and covariance. LebesgueMeasure(domain[, input_dim, normalized]) Lebesgue measure on a hyper-rectangle. Stopping criterion of a Bayesian quadrature method. IntegralVarianceTolerance(var_tol) Stop once the integral variance is below some tolerance. MaxNevals(max_nevals) Stop once a maximum number of integrand evaluations is reached. RandomPolicy(sample_func, batch_size[, rng]) Random sampling from an objective. RelativeMeanChange(rel_tol) Stop once the relative change of consecutive integral estimates are smaller than a tolerance.