pod.bootstrap_pod_ci

bootstrap_pod_ci(
    X,
    y,
    X_eval,
    threshold,
    model_type,
    model_params,
    bandwidth,
    dist_info,
    n_boot=1000,
    nuisance_ranges=None,
    n_jobs=None,
    feature_names=None,
    poi_names=None,
    confidence_levels=None,
    nuisance_dists=None,
)

Estimates Confidence Bounds for the PoD curve via Bootstrapping.

This function resamples the original data with replacement n_boot times. For each resample, it refits the Mean Model (dynamically rebuilding either a Polynomial or Kriging model), recalculates residuals, and generates a new PoD curve. If Kriging is selected, the optimizer is disabled during bootstrapping to remain computationally tractable.

Parameters

Name	Type	Description	Default
X	np.ndarray	Original input data.	required
y	np.ndarray	Original outcome data.	required
X_eval	np.ndarray	Grid points for evaluation.	required
threshold	float	Detection threshold.	required
model_type	str	The type of mean model (‘Polynomial’ or ‘Kriging’).	required
model_params	Any	Model parameters (integer degree for Poly, kernel for Kriging).	required
bandwidth	float	Smoothing bandwidth (fixed from original fit).	required
dist_info	Tuple[str, Tuple]	Error distribution (fixed from original fit).	required
n_boot	int	Number of bootstrap iterations. Defaults to 1000.	1000
nuisance_ranges	dict	Nuisance ranges.	None
n_jobs	int | None	Number of CPU cores to use.	None
feature_names	list	Names of all feature columns in X.	None
poi_names	list	Names of the parameters of interest (PoIs).	None
confidence_levels	list | None	Specific confidence levels (e.g. [50, 90, 95, 99]) to return curves for. If provided, returns a dict of {level: (lower, upper)}. Otherwise, returns the standard Tuple (lower_95, upper_95).	None
nuisance_dists	dict	Custom nuisance distribution configuration.	None

Returns

Name	Type	Description
	Tuple[np.ndarray, np.ndarray] | dict	Tuple[np.ndarray, np.ndarray] | dict: If confidence_levels is None: - lower_ci: The 2.5th percentile PoD curve (Lower 95% Bound). - upper_ci: The 97.5th percentile PoD curve (Upper 95% Bound). If confidence_levels is a list: - dict: Mapping each confidence level to its (lower_ci, upper_ci) curves.