pygwb.pe.GWBModel

class pygwb.pe.GWBModel(baselines=None, model_name=None, polarizations=None)[source]

Bases: Likelihood

Generic model, contains the definition of likelihood:

\[p(\hat{C}^{IJ}(f_k) | \mathbf{\Theta}) \propto\exp\left[ -\frac{1}{2} \sum_{IJ}^N \sum_k \left(\frac{\hat{C}^{IJ}(f_k) - \Omega_{\rm M}(f_k|\mathbf{\Theta})}{\sigma_{IJ}(f_k)}\right)^2 \right],\]

where \(\Omega_{\rm M}(f_k|\mathbf{\Theta})\) is the model being fit to data, and \(\mathbf{\Theta}\) are the model’s parameters.

The noise likelihood is given by setting \(\Omega_{\rm M}(f_k|\mathbf{\Theta})=0\).

__init__(baselines=None, model_name=None, polarizations=None)[source]

See also

bilby.Likelihood: More information here.

__call__(*args, **kwargs): Call self as a function.

Methods

`__init__`([baselines, model_name, polarizations])
`log_likelihood`()	Function for evaluating log likelihood of detector network.
`log_likelihood_IJ`(baseline, freq_mask[, noise])	Function for evaluating log likelihood of IJ baseline pair.
`log_likelihood_ratio`()	Difference between log likelihood and noise log likelihood
`model_function`()	Function for evaluating model.
`noise_log_likelihood`()	Function for evaluating noise log likelihood of detector network.
`parameters`()	Parameters to be inferred from the data.

Attributes

`marginalized_parameters`
`meta_data`

log_likelihood()[source]: Function for evaluating log likelihood of detector network.

log_likelihood_IJ(baseline, freq_mask, noise=False)[source]

Function for evaluating log likelihood of IJ baseline pair.

Parameters:

baseline (pygwb.Baseline) – Baseline for which to run parameter estimation on.
noise (bool, optional) – Parameter to indicate whether the likelihood should be evaluated assuming the signal model, or assuming only noise is present in the data.

Returns:

logL_IJ: float: Log likelihood value for the IJ baseline pair.

log_likelihood_ratio()

Difference between log likelihood and noise log likelihood

Returns:

float

abstract model_function()[source]: Function for evaluating model.

noise_log_likelihood()[source]: Function for evaluating noise log likelihood of detector network.

abstract parameters()[source]: Parameters to be inferred from the data. Should return a dictionary.