BayesianSequentialDeviationOED

class pyapprox.expdesign.BayesianSequentialDeviationOED(ndesign_candidates, obs_fun, noise_std, prior_variable, out_quad_opts, in_quad_opts, qoi_fun=None, obs_process=None, deviation_fun=<function oed_standard_deviation>, pred_risk_fun=<function oed_prediction_average>, data_risk_fun=<function oed_data_expectation>, nprocs=1, max_ncollected_obs=2)

Bases: BayesianSequentialOED, BayesianBatchDeviationOED

Compute closed-loop OED by minimizing the deviation on the push forward of the posterior through a QoI model.

Methods Summary

compute_expected_utility(...[, return_all])

Compute the expected utility.

Methods Documentation

compute_expected_utility(collected_design_indices, new_design_indices, return_all=False)

Compute the expected utility. Using the current posterior as the new prior.

Parameters:

collected_design_indicesnp.ndarray (nobs)

The indices into the qoi vector associated with the collected observations

new_design_indicesnp.ndarray (nnew_obs)

The indices into the qoi vector associated with new design locations under consideration

Notes

Passing None for collected_design_indices will ensure only obs at new_design indices is used to evaluate likelihood the data at collected indices is incoroporated into the inner and outer loop weights