BayesianBatchKLOED

class pyapprox.expdesign.BayesianBatchKLOED(ndesign_candidates, obs_fun, noise_std, prior_variable, out_quad_opts, in_quad_opts, nprocs=1, max_ncollected_obs=2, ndata_per_candidate=1, data_risk_fun=<function oed_data_expectation>)

Bases: AbstractBayesianOED

Compute open-loop OED my maximizing KL divergence between the prior and posterior.

Methods Summary

compute_expected_utility(...[, return_all])	return_all true used for debugging returns more than just utilities and also returns itermediate data useful for testing
populate()

Methods Documentation

compute_expected_utility(collected_design_indices, new_design_indices, return_all=False)

return_all true used for debugging returns more than just utilities and also returns itermediate data useful for testing

populate()