functional.models.markov_time_series()

Sample a Markov (state-space) latent over the full horizon.

Usage

Source

functional.models.markov_time_series(
    h,
    name,
    init_carry,
    transition,
    xs=None,
    *,
    plates=(),
    reparam_config=None
)

In-sample steps run in a numpyro.contrib.control_flow.scan with site name; when forecasting, horizon steps run in a second scan with site f"{name}_future" seeded by the final in-sample carry. The guide never sees the future site (same invariant as time_series()), and under posterior replay the carry is a deterministic function of the replayed draws, so the forecast is conditioned through the state.

Parameters

h: Horizon

The horizon for the current model call (see Horizon).

name: str

Base sample-site name for the in-sample latent scan.

init_carry: Any

Initial carry passed to the first transition.

transition: Transition

Per-step (carry, x_t) -> (dist_t, carry_fn) callable; the wrapper owns the numpyro.sample statement.

xs: Array | None = None

Optional exogenous inputs over the full horizon with time at axis -2, moved into scan layout internally; None for autonomous dynamics.

plates: Sequence[tuple[str, int]] = ()


(name, size) pairs opened inside the scan body around the sample statement (the only placement NumPyro supports for scan + plate).

reparam_config: Mapping[str, Reparam] | None = None

Site-name -> ~numpyro.infer.reparam.Reparam mapping applied inside the scan body.

Returns

Array

The latent over the full horizon in package layout (*plate_batch, duration, obs).

Raises

ValueError

If forecasting without observed data, if the per-step shape lacks the observation dimension, or if an enclosing plate is detected.