Explain the Kalman filter and state-space models. What are the predict and update steps actually doing?
The Kalman filter is optimal Bayesian tracking under linear-Gaussian assumptions, and it amounts to two steps repeated forever. The signal is explaining what the gain trades off and when the assumptions break. Here is the answer.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
The Kalman filter is optimal Bayesian tracking under linear-Gaussian assumptions, and it amounts to two steps repeated forever. The signal is explaining what the gain trades off and when the assumptions break. Here is the answer.
Lead with where the obvious approach breaks, because that is the judgment they are screening for — most candidates jump straight to the happy path and lose the room.
Then walk the failure back through the pipeline in order, naming the one metric the customer's exec sponsor actually cares about before you propose the fix.
