pygsti.tools.likelihoodfns.logl_max

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pygsti.tools.likelihoodfns.logl_max#

logl_max(model, dataset, circuits=None, poisson_picture=True, op_label_aliases=None, mdc_store=None)#

The maximum log-likelihood possible for a DataSet.

That is, the log-likelihood obtained by a maximal model that can fit perfectly the probability of each circuit.

Parameters:

  • model (Model) – the model, used only for circuit compilation

  • dataset (DataSet) – the data set to use.

  • circuits (list of (tuples or Circuits), optional) – Each element specifies a circuit to include in the max-log-likelihood sum. Default value of None implies all the circuits in dataset should be used.

  • poisson_picture (boolean, optional) – Whether the Poisson-picture maximum log-likelihood should be returned.

  • op_label_aliases (dictionary, optional) – Dictionary whose keys are operation label “aliases” and whose values are tuples corresponding to what that operation label should be expanded into before querying the dataset. Defaults to the empty dictionary (no aliases defined) e.g. op_label_aliases[‘Gx^3’] = (‘Gx’,’Gx’,’Gx’)

  • mdc_store (ModelDatasetCircuitsStore, optional) – An object that bundles cached quantities along with a given model, dataset, and circuit list. If given, model and dataset and circuits should be set to None.

Return type:

float

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