mixle.stats.compute.sampling_api module

A single sample() entry point that draws from any samplable mixle object.

sample(model, size) dispatches on the kind of model so distributions, conjugate posteriors, relations, field posteriors and latent posteriors all read the same way:

mixle.stats.sample(gauss, 100)                 # 100 iid observations
mixle.stats.sample(posterior, 50)              # 50 parameter draws from a conjugate posterior
mixle.stats.sample(assignment, 10, temperature=2.0)   # 10 Gibbs-weighted relation members
mixle.stats.sample(field_post, 100)            # 100 joint field draws (dict per node)
mixle.stats.sample(q_latent, 5)                # 5 latent-variable draws

A shared rng (a numpy.random.RandomState) makes a whole pipeline reproducible: it is threaded into the relation / field / latent draws directly, and for a distribution / conjugate posterior the per-call stream is seeded from it, so one rng drives independent reproducible streams across many sample() calls.

sample(model, size=None, *, seed=None, rng=None, **kwargs)

Draw sample(s) from any samplable mixle object.

Parameters:

• model (Any) – a distribution, conjugate posterior, Relation, FieldPosterior or LatentPosterior.

• size (int | None) – None returns a single draw in the object’s natural type; an int returns a collection (an array for homogeneous leaves, a list / dict-of-arrays for structured draws).

• seed (int | None) – scalar seed for the draw (ignored if rng is given).

• rng (RandomState | None) – a shared RandomState for reproducible, composable streams; takes precedence over seed.

• **kwargs (Any) – forwarded to the underlying sampler – e.g. temperature / k / uniform for a relation, nodes for a field posterior, batched for a distribution.

Returns:

A single draw (size=None) or a collection of size draws.

Raises:

TypeError – if model is not a recognized samplable object.

Return type:

Any

register_sample_dispatch(fn)

Register a sample() handler for a type the core layer must not import. Returns fn.