models.markov_series()

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

Usage

models.markov_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 innovations()), 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: Carry: Initial carry passed to the first transition.
transition: Transition[Carry]: Per-step (carry, x_t) -> (dist_t, carry_fn) callable; the wrapper owns the numpyro.sample statement.
xs: PyTree[Array] | None = None: Optional exogenous inputs over the full horizon: a PyTree of arrays with time at axis -2 (a single array, a tuple, a dict, …), moved leaf by leaf 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 (only reachable with a hand-built Horizon: Horizon.from_data() never sets future > 0 without data), if the per-step shape lacks the observation dimension, or if an enclosing plate is detected.