Kalman Filter
The Kalman filter is the recursive Bayesian update for a linear-Gaussian state-space model: predict the hidden state, then correct with the new observation.
Definition
Kalman Filter refers to gaussian state-space model: predict the hidden state, then correct with the new observation. Keep that definition fixed when comparing series, managers, or regimes — renaming the same tape does not create a new signal.
Why it matters
It is a named object desks use to frame risk, positioning, or process. When gaussian state-space model: predict the hidden state, then correct with the new observation shifts, related hedges, limits, and narratives usually need an explicit update rather than a quiet assumption.
Case
Suppose a desk is positioned for the opposite of what kalman filter is saying. If gaussian state-space model: predict the hidden state, then correct with the new observation moves against that book, the first question is not “is the story clever?” but whether size, hedges, and stop logic still match the observation.
How to read it
Keep the definition fixed, then challenge it with cross-checks before sizing. Prefer a short written null hypothesis for Kalman Filter: what would falsify the current reading in the next window?