Smoothers

Smoother computes posterior smoothing distributions \(p(x_t \mid y_{1:T})\) and adds the corresponding marginal log-likelihood factor for parameter inference, mirroring Filter semantics.

Usage

from dynestyx import DiscreteTimeSimulator, Smoother
from dynestyx.inference.smoother_configs import KFSmootherConfig

with DiscreteTimeSimulator(n_simulations=4):
    with Smoother(
        smoother_config=KFSmootherConfig(
            filter_source="cd_dynamax",
            record_smoothed_states_mean=True,
        )
    ):
        samples = model(
            obs_times=obs_times,
            obs_values=obs_values,
            predict_times=future_times,
        )

obs_times and obs_values are required together. Smoother consumes them, adds a marginal log-likelihood factor, and can record deterministic sites such as f_smoothed_states_mean, f_smoothed_states_cov, and f_smoothed_states_cov_diag.

Prediction Semantics

For this release, smoother-backed prediction is intentionally future-only: every predict_time must satisfy predict_time >= max(obs_times). The downstream simulator rolls out from the final smoothed state distribution.

Prediction times inside the smoothing window currently raise a clear error instead of silently using incorrect indexing or backend-specific missing-data behavior.

Support Matrix

Model class	Config	Backend
Discrete linear-Gaussian	KFSmootherConfig	cuthbert, cd_dynamax
Discrete nonlinear Gaussian	EKFSmootherConfig	cuthbert, cd_dynamax
Discrete nonlinear Gaussian	UKFSmootherConfig	cd_dynamax
Discrete non-Gaussian/nonlinear	PFSmootherConfig	cuthbert
Continuous-discrete linear-Gaussian	ContinuousTimeKFSmootherConfig	cd_dynamax
Continuous-discrete nonlinear Gaussian	ContinuousTimeEKFSmootherConfig	cd_dynamax

Smoother dataclass

Bases: BaseSmootherLogFactorAdder

Performs Bayesian smoothing to compute the smoothing distribution p(x_t | y_{1:T}).