spey.backends.default_pdf.DefaultPDFBase

class spey.backends.default_pdf.DefaultPDFBase(signal_yields: ndarray, background_yields: ndarray, data: ndarray, covariance_matrix: ndarray | Callable[[ndarray], ndarray] | None = None, signal_uncertainty_configuration: Dict[str, Any] | None = None)

Default PDF backend base

Parameters:

• signal_yields (np.ndarray) – signal yields

• background_yields (np.ndarray) – background yields

• data (np.ndarray) – observed yields

• covariance_matrix (np.ndarray) –

  covariance matrix. The dimensionality of each axis has to match with background_yields, signal_yields, and data inputs.

  Warning

  The diagonal terms of the covariance matrix involves squared absolute background uncertainties. In case of uncorralated bins user should provide a diagonal matrix with squared background uncertainties.

• signal_uncertainty_configuration (Dict[Text, Any]], default None) –

  Configuration input for signal uncertainties

  • absolute_uncertainties (List[float]): Absolute uncertainties for the signal

  • absolute_uncertainty_envelops (List[Tuple[float, float]]): upper and lower

    uncertainty envelops

  • correlation_matrix (List[List[float]]): Correlation matrix

  • third_moments (List[float]): diagonal elemetns of the third moment

Note

To enable a differentiable statistical model, all inputs are wrapped with autograd.numpy.array() function.

__init__(signal_yields: ndarray, background_yields: ndarray, data: ndarray, covariance_matrix: ndarray | Callable[[ndarray], ndarray] | None = None, signal_uncertainty_configuration: Dict[str, Any] | None = None)

Methods

__init__(signal_yields, background_yields, data)
asimov_negative_loglikelihood([poi_test, ...])	Compute negative log-likelihood at fixed \(\mu\) for Asimov data.
combine(other, **kwargs)	A routine to combine to statistical models.
config([allow_negative_signal, poi_upper_bound])	Model configuration.
expected_data(pars[, include_auxiliary])	Compute the expected value of the statistical model
get_hessian_logpdf_func([expected, data])	Currently Hessian of \(\log\mathcal{L}(\mu, \theta)\) is only used to compute variance on \(\mu\).
get_logpdf_func([expected, data])	Generate function to compute \(\log\mathcal{L}(\mu, \theta)\) where \(\mu\) is the parameter of interest and \(\theta\) are nuisance parameters.
get_objective_function([expected, data, do_grad])	Objective function i.e. twice negative log-likelihood, \(-2\log\mathcal{L}(\mu, \theta)\).
get_sampler(pars)	Retreives the function to sample from.
minimize_asimov_negative_loglikelihood([...])	A backend specific method to minimize negative log-likelihood for Asimov data.
minimize_negative_loglikelihood([expected, ...])	A backend specific method to minimize negative log-likelihood.
negative_loglikelihood([poi_test, expected])	Backend specific method to compute negative log-likelihood for a parameter of interest \(\mu\).

Attributes

constraints	Constraints to be used during optimisation process
signal_uncertainty_configuration
author	Author of the backend
constraint_model	retreive constraint model distribution
is_alive	Returns True if at least one bin has non-zero signal yield.
main_model	retreive the main model distribution
name	Name of the backend
spey_requires	Spey version required for the backend
version	Version of the backend