A way to update factor weights smoothly over time as their informational content drifts, rather than re-fitting from scratch.
Kalman 1960 / West-Harrison 1997 dynamic linear model: each factor's coefficient on forward returns is treated as a slowly-evolving random walk. We update the posterior on each new observation. This avoids the choice between "use the prior forever" (rigid) and "re-fit on a rolling window" (jumpy). Activates as forward returns accumulate post-2026-05-16.