SamplesSummary.from_array()

classmethod SamplesSummary.from_array(a, quantiles=(0.05, 0.5, 0.95), hdi_prob=0.9, selected=None, deselected=None, per_chain=False, name='v', which=('mean', 'sd', 'var', 'quantiles', 'hdi', 'ess_bulk', 'ess_tail', 'rhat', 'mcse_mean', 'mcse_sd'))

Initializes the summary from an array of samples.

Parameters:

• a (Any) – The array of samples to summarize. Expected to have leading dimensions (nchains, ndraws, ...).

• quantiles (Sequence[float], default: (0.05, 0.5, 0.95)) – Posterior quantile probabilities to compute when "quantiles" is included in which.

• hdi_prob (float, default: 0.9) – Posterior probability mass of the highest density interval to compute when "hdi" is included in which.

• selected (list[str] | None, default: None) – Allow to get a summary only for a subset of the position keys.

• deselected (list[str] | None, default: None) – Allow to get a summary only for a subset of the position keys.

• per_chain (bool, default: False) – If True, the summary is calculated on a per-chain basis. Certain measures like rhat are not available if per_chain is True.

• name (str, default: 'v') – Variable name to use for labelling in to_dataframe().

• which (Sequence[Literal['mean', 'sd', 'var', 'quantiles', 'hdi', 'ess_bulk', 'ess_tail', 'rhat', 'mcse_mean', 'mcse_sd']], default: ('mean', 'sd', 'var', 'quantiles', 'hdi', 'ess_bulk', 'ess_tail', 'rhat', 'mcse_mean', 'mcse_sd')) – Names of the summary statistics to compute. Supported values are the same as for Summary.

Return type:

SamplesSummary