pmrf.fitting.BayesianFitter

class pmrf.fitting.BayesianFitter(model, *, likelihood_kind=None, likelihood_params=None, feature_sigmas=None, **kwargs)

Bases: BaseFitter

A base class for Bayesian fitting methods.

This class extends BaseFitter by adding the concept of a likelihood function.

Parameters:

• model (Model)

• likelihood_kind (str | None)

• likelihood_params (dict[str, Parameter])

• feature_sigmas (list[str] | None)

__init__(model, *, likelihood_kind=None, likelihood_params=None, feature_sigmas=None, **kwargs)

Initializes the BayesianFitter.

Parameters:

• model (Model) – The parametric pmrf model to be fitted.

• likelihood_kind (str, optional) – The kind of likelihood to use. Can be either ‘gaussian’ or ‘multivariate_gaussian’. Defaults internally to ‘gaussian’ for one-port fits, and ‘multivariate_gaussian’ for greater port fits. For ‘gaussian’, a single likelihood parameter, ‘sigma’, is needed. For ‘multivariate_gaussian’, either multiple standard deviations ‘sigma_0’, ‘sigma_1’, …, ‘sigma_N’ may be passed, where N is the number of features, or an arbitrary number of arbitrarily named likelihood parameters may be passed, along with a list of strings feature_sigmas of size N containing the names of the likelihood parameters to use for each feature.

• likelihood_params (dict[str, Parameter], optional) – A dictionary of likelihood parameters to use for the likelihood function.

• feature_sigmas (list[str], optional) – A list of sigma names for each feature. Only used when likelihood_kind is ‘multivariate_gaussian’.

• **kwargs – Additional arguments forwarded to BaseFitter.

Return type:

None

Methods

__delattr__(name, /)	Implement delattr(self, name).
__dir__()	Default dir() implementation.
__eq__(value, /)	Return self==value.
__format__(format_spec, /)	Default object formatter.
__ge__(value, /)	Return self>=value.
__getattribute__(name, /)	Return getattr(self, name).
__gt__(value, /)	Return self>value.
__hash__()	Return hash(self).
__init__(model, *[, likelihood_kind, ...])	Initializes the BayesianFitter.
__init_subclass__	This method is called when a class is subclassed.
__le__(value, /)	Return self<=value.
__lt__(value, /)	Return self<value.
__ne__(value, /)	Return self!=value.
__new__(**kwargs)
__reduce__()	Helper for pickle.
__reduce_ex__(protocol, /)	Helper for pickle.
__repr__()	Return repr(self).
__setattr__(name, value, /)	Implement setattr(self, name, value).
__sizeof__()	Size of object in memory, in bytes.
__str__()	Return str(self).
__subclasshook__	Abstract classes can override this to customize issubclass().
_create_context(measured, *[, ...])	Create a BayesianContext for the fitting process.
_run_algorithm(context, **kwargs)	Executes the fitting algorithm.
_run_context(ctx[, plot_params, ...])	Execute the Bayesian fitting process within a context
fit(measured, **kwargs)	Fits the model to measured data.
fit_submodels(measured, **kwargs)	Fits the submodels.

Attributes

__abstractmethods__
__annotations__
__dict__
__doc__
__module__
__slots__
__weakref__	list of weak references to the object (if defined)
_abc_impl

__init__(model, *, likelihood_kind=None, likelihood_params=None, feature_sigmas=None, **kwargs)

Initializes the BayesianFitter.

Parameters:

• model (Model) – The parametric pmrf model to be fitted.

• likelihood_kind (str, optional) – The kind of likelihood to use. Can be either ‘gaussian’ or ‘multivariate_gaussian’. Defaults internally to ‘gaussian’ for one-port fits, and ‘multivariate_gaussian’ for greater port fits. For ‘gaussian’, a single likelihood parameter, ‘sigma’, is needed. For ‘multivariate_gaussian’, either multiple standard deviations ‘sigma_0’, ‘sigma_1’, …, ‘sigma_N’ may be passed, where N is the number of features, or an arbitrary number of arbitrarily named likelihood parameters may be passed, along with a list of strings feature_sigmas of size N containing the names of the likelihood parameters to use for each feature.

• likelihood_params (dict[str, Parameter], optional) – A dictionary of likelihood parameters to use for the likelihood function.

• feature_sigmas (list[str], optional) – A list of sigma names for each feature. Only used when likelihood_kind is ‘multivariate_gaussian’.

• **kwargs – Additional arguments forwarded to BaseFitter.

Return type:

None