User Reference Guide¶
pyapprox provides functions for surrogate modeling, sensitivity analysis, quadrature, inference, optimal experimental design and multi-fidelity modeling
pyapprox.approximate.approximate() function produces
response surface approximations from training data.
The approximate function supports the following methods
Supervised active learning¶
pyapprox.approximate.adaptive_approximate() function produces adaptive
response surface approximations of a function
fun, As the approximation is built the function being approximated is sampled at locations that greedily minimize an estimate of error.
The adaptive_approximate function supports the following methods
Surrogate based global sensitivity analysis¶
The following functions can be used to extract sensitivity metrics analytically from a surrogate.
The following functions can be used to visualize variance based sensitivity measures
Local sensitivity analysis¶
morris sensitivity indices.
Surrogate based quadrature¶
The following functions can be used to compute the mean and variance analytically from a surrogate.
pyapprox.bayesian_inference.markov_chain_monte_carlo.run_bayesian_inference_gaussian_error_model() can be used to draw samples from the posterior distribution of variables of a model conditioned on a set of observations with Gaussian noise.
Optimal experimental design¶
Optimal experimental designs for m-estimators such as least squares and quantile regression can be computed with