ACVMF

class pyapprox.control_variate_monte_carlo.ACVMF(cov, costs)

Bases: object

Methods Summary

__call__(values)	Call self as a function.
allocate_samples(target_cost, **kwargs)
generate_data(nhf_samples, nsample_ratios, …)
get_covariance()
get_nsamples(nhf_samples, nsample_ratios)
get_rsquared(nsample_ratios)
get_variance(nhf_samples, nsample_ratios)
jacobian(x)
objective(x)
variance_reduction(nsample_ratios)	This is not the variance reduction relative to the equivalent Monte Carlo estimator.

Methods Documentation

__call__(values)

Call self as a function.

allocate_samples(target_cost, **kwargs)

generate_data(nhf_samples, nsample_ratios, generate_samples, model_ensemble)

get_covariance()

get_nsamples(nhf_samples, nsample_ratios)

get_rsquared(nsample_ratios)

get_variance(nhf_samples, nsample_ratios)

jacobian(x)

objective(x)

variance_reduction(nsample_ratios)

This is not the variance reduction relative to the equivalent Monte Carlo estimator. A variance reduction can be smaller than one and still correspond to a multi-fidelity estimator that has a larger variance than the single fidelity Monte Carlo that uses the equivalent number of high-fidelity samples