meridian_tools.log_likelihood

Log-likelihood computation and attachment for Meridian models.

Module: meridian_tools.log_likelihood

Functions

compute_log_likelihood_dataset

def compute_log_likelihood_dataset(
    meridian_model: Any,
) -> xr.Dataset

Compute the pointwise log-likelihood dataset for a fitted Meridian model.

This function reconstructs the joint distribution from the posterior samples and computes observation-level log-likelihood values. It handles both geo-panel and national models.

The reconstruction recovers unsaved posterior parameters (e.g. geo deviations, tau_g_excl_baseline) that Meridian does not persist to InferenceData by default.

Parameters:

  • meridian_model — A fitted Meridian model with posterior samples and a compatible posterior_sampler_callable.

Returns: An xarray Dataset with a log_likelihood variable.

Raises: ModelSelectionError if the model does not expose the required internal reconstruction seams or lacks posterior samples.

attach_log_likelihood

def attach_log_likelihood(
    meridian_model: Any,
    *,
    in_place: bool = False,
) -> az.InferenceData

Attach a log_likelihood group to a Meridian model’s InferenceData.

If the model’s InferenceData already has a non-empty log_likelihood group, it is returned as-is (or the existing InferenceData is returned for in_place=True).

Parameters:

  • meridian_model — A fitted Meridian model.
  • in_place — If True, mutates meridian_model.inference_data directly. If False (default), returns a deep copy with the log_likelihood group attached. The original model is never modified.

Returns: An ArviZ InferenceData with a log_likelihood group.

Raises:

  • ModelSelectionError with reason_code="meridian_internal_seam_incompatible" if the Meridian version lacks the required private reconstruction methods.
  • ModelSelectionError with reason_code="requires_fitted_meridian_model" if the model has no posterior samples.
  • ModelSelectionError with reason_code="holdout_fit_unsupported" if the model was fitted with a holdout mask.

Example:

from meridian_tools.log_likelihood import attach_log_likelihood

# Non-mutating (default)
idata = attach_log_likelihood(fitted_model, in_place=False)
assert hasattr(idata, "log_likelihood")

# Mutating
attach_log_likelihood(fitted_model, in_place=True)
assert hasattr(fitted_model.inference_data, "log_likelihood")

Implementation notes

The reconstruction accesses three private methods on Meridian’s posterior_sampler_callable:

  • _get_joint_dist_unpinned
  • _prepare_latents_for_reconstruction
  • _reconstruct_posteriors

These are Meridian-internal and may change without notice. If any method is missing, a ModelSelectionError with reason_code="meridian_internal_seam_incompatible" is raised instead of crashing. See the Meridian integration notes for details on this coupling boundary.