likelihood

Module Contents

Classes

log_likelihood_chisq	log_likelihood_chisq is an object to obtain chi squared log likelihood.
log_likelihood_chisq_weighted_obs_type	log_likelihood_chisq_weighted_obs_type is an object to obtain chi squared
log_likelihood_chisq_weighted_filts_and_obs_type	log_likelihood_chisq_weighted_filts_and_obs_type is an object to obtain chi

class likelihood.log_likelihood_chisq(observations, *args, **kwargs)

Bases: object

log_likelihood_chisq is an object to obtain chi squared log likelihood. Instantiate this object with an observables object to indicate the observed observables.

Parameters:

observationsobservables

observables object, specified with the observations to be fit.

evaluate(model_observables)

class likelihood.log_likelihood_chisq_weighted_obs_type(observations, *args, **kwargs)

Bases: log_likelihood_chisq

log_likelihood_chisq_weighted_obs_type is an object to obtain chi squared log likelihood, while weighing different observation types equally. This weighting scheme may be useful for cases where number of a given observation type is much larger than that of another.

The output log likelihood is calculated in the following way: log_like_total = (log_like_t1)/(n_t1) + (log_like_t2)/(n_t2) + … (see e.g., Lam+ 2022)

Instantiate this object with an observables object to indicate the observed observables.

Parameters:

observationsobservables

observables object, specified with the observations to be fit.

evaluate(model_observables)

class likelihood.log_likelihood_chisq_weighted_filts_and_obs_type(observations, *args, **kwargs)

Bases: log_likelihood_chisq

log_likelihood_chisq_weighted_filts_and_obs_type is an object to obtain chi squared log likelihood, while weighing different observation types, and filters within each observation type equally. This weighting scheme may be useful for cases where number of a given observation type or filt is much larger than that of another.

The output log likelihood is calculated in the following way: log_like_total = (log_like_t1)/(n_t1) + (log_like_t2)/(n_t2) + … (see e.g., Lam+ 2022)

Instantiate this object with an observables object to indicate the observed observables.

Parameters:

observationsobservables

observables object, specified with the observations to be fit.

evaluate(model_observables)