models.innovations()

Sample conditionally iid per-step innovations over the full horizon.

Usage

Source

models.innovations(
    h,
    name,
    dist_fn,
    *,
    reparam=None,
)

The in-sample portion is sampled under plate("time", t) with the fixed site name; when forecasting, the horizon portion is sampled under a separate site f"{name}_future" and concatenated. The separate site keeps the guide shape fixed and lets Predictive draw the forecast suffix from the prior. Build the series arithmetically from the result (a random walk is jnp.cumsum(drift, axis=-2)); a latent whose per-step distribution depends on the previous state is markov_series(), and a deterministic error-feedback recursion driven by the observed series is ssoe().

Parameters

h: Horizon

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

name: str

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

dist_fn: Callable[[], dist.Distribution]

Zero-argument callable returning the per-step prior distribution.

reparam: Reparam | None = None

Optional reparameterization (e.g. LocScaleReparam) applied to both the in-sample and forecast sites.

Returns

Array

The latent over the full horizon with time at axis -2.