setu.mixed_nle_to_pymc#

setu.mixed_nle_to_pymc(mixed_nle, theta, x_observed, *, conditions=None, chunk_size=None, name='mixed_nle_likelihood', dims=None)[source]#

Convert trained MixedNLE to PyMC likelihood.

Validates that discrete columns contain integer values, then delegates to to_pymc (which accepts any likelihood estimator).

Parameters:

  • mixed_nle (MixedNLE) – Trained MixedNLE instance

  • theta (TensorVariable) – PyMC parameter variable

  • x_observed (Array) – Observed data (n_obs, x_dim) or (x_dim,) with continuous variables in first columns, discrete in last

  • 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

Example

with pm.Model():

theta = pm.Normal(“theta”, 0, 1, shape=2) likelihood = mixed_nle.to_pymc(theta, x_observed) trace = pm.sample(nuts_sampler=”blackjax”)