credible_interval

bayesflow.utils.numpy_utils.credible_interval(x: ndarray, prob: float = 0.95, axis: Sequence[int] | int = None, **kwargs) → ndarray

Compute credible interval from samples using quantiles.

Parameters:

xarray_like

Input array of samples from a posterior distribution or bootstrap samples.

probfloat, default 0.95

Coverage probability of the credible interval (between 0 and 1). For example, 0.95 gives a 95% credible interval.

axisSequence[int]

Axis or axes along which the credible interval is computed. Default is None (flatten array).

Returns:

a numpy array of shape (2, …) with the first dimension indicating the

lower and upper bounds of the credible interval.

Examples

>>> import numpy as np
>>> # Simulate posterior samples
>>> samples = np.random.normal(size=(10, 1000, 3))

>>> # Different coverage probabilities
>>> credible_interval(samples, prob=0.5, axis=1)  # 50% CI
>>> credible_interval(samples, prob=0.99, axis=1)  # 99% CI