spey.math.value_and_grad

spey.math.value_and_grad(statistical_model: StatisticalModel, expected: ExpectationType = ExpectationType.observed, data: List[float] | None = None) → Callable[[ndarray], Tuple[ndarray, ndarray]]

Retreive function to compute negative log-likelihood and its gradient.

New in version 0.1.6.

Parameters:

• statistical_model (StatisticalModel) – statistical model to be used.

• expected (ExpectationType) –

  Sets which values the fitting algorithm should focus and p-values to be computed.

  • observed: Computes the p-values with via post-fit prescriotion which means that the experimental data will be assumed to be the truth (default).

  • aposteriori: Computes the expected p-values with via post-fit prescriotion which means that the experimental data will be assumed to be the truth.

  • apriori: Computes the expected p-values with via pre-fit prescription which means that the SM will be assumed to be the truth.

• data (List[float], default None) – input data that to fit. If None observed data will be used.

Returns:

negative log-likelihood and its gradient with respect to nuisance parameters

Return type:

Callable[[np.ndarray], Tuple[np.ndarray, np.ndarray]]