NonLinearAlphabetOptimalDesign

class pyapprox.expdesign.NonLinearAlphabetOptimalDesign(criteria, design_factors, noise_multiplier=None, opts=None, regression_type='lstsq')

Bases: AlphabetOptimalDesign

Construct minimax optimal experimental designs of non-linear models by sampling fisher information matrix at multiple uncertain parameter realizations.

Methods Summary

bayesian_objective_jacobian_components(...)
setup_minimax_nonlinear_constraints(...)
setup_objective(criteria, homog_outer_prods, ...)
solve_nonlinear_bayesian(samples, design_samples)
solve_nonlinear_minimax(parameter_samples, ...)

Methods Documentation

bayesian_objective_jacobian_components(parameter_samples, design_samples)

setup_minimax_nonlinear_constraints(parameter_samples, design_samples)

setup_objective(criteria, homog_outer_prods, design_factors, noise_multiplier, opts)

solve_nonlinear_bayesian(samples, design_samples, sample_weights=None, options=None, return_full=False, x0=None)

solve_nonlinear_minimax(parameter_samples, design_samples, options=None, return_full=False, x0=None)