models.markov_series()

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

Usage

Source

models.markov_series(
    h,
    name,
    init_carry,
    transition,
    xs=None,
    *,
    advance=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 callable returning the distribution of the next latent (see Transition); 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.

advance: Advance[Carry] | None = None

Optional (carry, z_t, x_t) -> carry (see Advance) that builds the next carry from the sampled latent; None means the carry is the latent, carry_{t+1} = z_t, so init_carry must be a single array shaped like one draw. A vector autoregression with p lags keeps a (p, obs) window here.

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, if an enclosing plate is detected, or if advance is omitted and init_carry is not a single array with the shape and dtype of a draw.