SamplesSummary

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SamplesSummary#

class liesel.goose.SamplesSummary(samples, quantiles=(0.05, 0.5, 0.95), hdi_prob=0.9, selected=None, deselected=None, per_chain=False, which=('mean', 'sd', 'var', 'quantiles', 'hdi', 'ess_bulk', 'ess_tail', 'rhat', 'mcse_mean', 'mcse_sd'))[source]#

Bases: object

A summary object based on a dictionary of samples.

Offers two main use cases:

  1. The summary object can be turned into a DataFrame

    using to_dataframe().

  2. Programmatically access summary statistics via quantities[quantity_name][var_name]. Please refer to the documentation of the attribute quantities for details.

Parameters:

  • samples (dict[str, Any]) – The dictionary of samples to summarize.

  • hdi_prob (float, default: 0.9) – Level on which to return posterior highest density intervals.

  • 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.

Notes

This class is still considered experimental. The API may still undergo larger changes.

Methods

aggregate_diagnostics([by])

Aggregates effective sample sizes (ESS) and rhat.

from_array(a[, quantiles, hdi_prob, ...])

Initializes the summary from an array of samples.

to_dataframe()

Turns Summary object into a DataFrame object.

Attributes

config

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