c3.libraries.estimators

Collection of estimator functions, to compare two sets of (noisy) data.

Module Contents

c3.libraries.estimators.estimator_reg_deco(func)

Decorator for making registry of functions

c3.libraries.estimators.mean_dist(exp_values, sim_values, exp_stds, shots)

Return the root mean squared of the differences.

c3.libraries.estimators.median_dist(exp_values, sim_values, exp_stds, shots)

Return the median of the differences.

c3.libraries.estimators.rms_dist(exp_values, sim_values, exp_stds, shots)

Return the root mean squared of the differences.

c3.libraries.estimators.mean_sim_stds_dist(exp_values, sim_values, exp_stds, shots)

Return the mean of the distance in number of exp_stds away.

c3.libraries.estimators.rms_sim_stds_dist(exp_values, sim_values, exp_stds, shots)

Return the root mean squared of the differences measured in exp_stds.

c3.libraries.estimators.mean_exp_stds_dist(exp_values, sim_values, exp_stds, shots)

Return the mean of the distance in number of exp_stds away.

c3.libraries.estimators.rms_exp_stds_dist(exp_values, sim_values, exp_stds, shots)

Return the root mean squared of the differences measured in exp_stds.

c3.libraries.estimators.std_of_diffs(exp_values, sim_values, exp_stds, shots)

Return the std of the distances.

c3.libraries.estimators.neg_loglkh_binom(exp_values, sim_values, exp_stds, shots)

Average likelihood of the experimental values with binomial distribution.

Return the likelihood of the experimental values given the simulated values, and given a binomial distribution function.

c3.libraries.estimators.neg_loglkh_binom_norm(exp_values, sim_values, exp_stds, shots)

Average likelihood of the exp values with normalised binomial distribution.

Return the likelihood of the experimental values given the simulated values, and given a binomial distribution function that is normalised to give probability 1 at the top of the distribution.

c3.libraries.estimators.neg_loglkh_gauss(exp_values, sim_values, exp_stds, shots)

Likelihood of the experimental values.

The distribution is assumed to be binomial (approximated by a gaussian).

c3.libraries.estimators.neg_loglkh_gauss_norm(exp_values, sim_values, exp_stds, shots)

Likelihood of the experimental values.

The distribution is assumed to be binomial (approximated by a gaussian) that is normalised to give probability 1 at the top of the distribution.

c3.libraries.estimators.neg_loglkh_gauss_norm_sum(exp_values, sim_values, exp_stds, shots)

Likelihood of the experimental values.

The distribution is assumed to be binomial (approximated by a gaussian) that is normalised to give probability 1 at the top of the distribution.

c3.libraries.estimators.neg_loglkh_multinom(exp_values, sim_values, exp_stds, shots)

Average likelihood of the experimental values with multinomial distribution.

Return the likelihood of the experimental values given the simulated values, and given a multinomial distribution function.

c3.libraries.estimators.neg_loglkh_multinom_norm(exp_values, sim_values, exp_stds, shots)

Average likelihood of the experimental values with multinomial distribution.

Return the likelihood of the experimental values given the simulated values, and given a multinomial distribution function that is normalised to give probability 1 at the top of the distribution.