setu.discrete_nle_to_pymc#

setu.discrete_nle_to_pymc(discrete_nle, theta, x_observed, *, conditions=None, chunk_size=None, name='discrete_nle_likelihood', dims=None)[source]#

Convert trained DiscreteNLE to PyMC likelihood.

Delegates to to_pymc (which accepts any likelihood estimator via GenericLogpOp). DiscreteNLE shares the log_prob interface with NLE/MixedNLE.

Parameters:

  • discrete_nle (LikelihoodEstimator) – Trained DiscreteNLE instance

  • theta (TensorVariable) – PyMC parameter variable

  • x_observed (Array) – Observed count data (n_obs, x_dim) or (x_dim,)

  • conditions (Array | None) – Optional experimental conditions (n_obs, condition_dim)

  • chunk_size (int | None) – If provided, process observations in chunks of this size to reduce peak memory usage.

  • name (str) – Name for the PyMC potential

  • dims (tuple[str, ...] | None) – PyMC dimension names (for coords)

Return type:

Potential

Returns:

PyMC Potential adding log p(x_observed | theta, conditions) to model