pyhf.infer.mle.fit¶

pyhf.infer.mle.fit(data, pdf, init_pars=None, par_bounds=None, **kwargs)[source]¶

Run a unconstrained maximum likelihood fit.

Example

>>> import pyhf
>>> pyhf.set_backend("numpy")
>>> model = pyhf.simplemodels.hepdata_like(
...     signal_data=[12.0, 11.0], bkg_data=[50.0, 52.0], bkg_uncerts=[3.0, 7.0]
... )
>>> observations = [51, 48]
>>> data = pyhf.tensorlib.astensor(observations + model.config.auxdata)
>>> pyhf.infer.mle.fit(data, model, return_fitted_val=True)
(array([0.        , 1.0030512 , 0.96266961]), array([24.98393521]))

Parameters

data (tensor) – The data
pdf (Model) – The statistical model adhering to the schema model.json
init_pars (list) – Values to initialize the model parameters at for the fit
par_bounds (list of lists or tuples) – The extrema of values the model parameters are allowed to reach in the fit
kwargs – Keyword arguments passed through to the optimizer API

Returns

See optimizer API