pmrf.fitting.BayesianResults

class pmrf.fitting.BayesianResults(measured=None, initial_model=None, fitted_model=None, solver_results=None, settings=None, fitter=None)

Bases: FitResults

Parameters:

• measured (Network | NetworkCollection | None)

• initial_model (Model | None)

• fitted_model (Model | None)

• solver_results (Any)

• settings (FitSettings | None)

• fitter (BaseFitter | None)

__init__(measured=None, initial_model=None, fitted_model=None, solver_results=None, settings=None, fitter=None)

Parameters:

• measured (Network | NetworkCollection | None)

• initial_model (Model | None)

• fitted_model (Model | None)

• solver_results (Any | None)

• settings (FitSettings | None)

• fitter (BaseFitter | None)

Return type:

None

Methods

__delattr__(name, /)	Implement delattr(self, name).
__dir__()	Default dir() implementation.
__eq__(other)	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.
__init__([measured, initial_model, ...])
__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().
decode_solver_results(group)
encode_solver_results(group)
load_hdf(path)
save_hdf(path[, metadata])

Attributes

__annotations__
__dataclass_fields__
__dataclass_params__
__dict__
__doc__
__hash__
__match_args__
__module__
__weakref__	list of weak references to the object (if defined)
fitted_model
initial_model
measured
settings
solver_results

abstract prior_samples()

Return type:

Array

abstract posterior_samples()

Return type:

Array

update_priors(train_dist=None, **train_kwargs)

Parameters:

train_dist (TrainableDistributionT | None)