Bayesian Filtering
The recursive predict–update loop behind all filters.
Why it matters in robotics
Bayesian filtering is the recursive predict-update backbone underneath every estimator a robot uses — Kalman, EKF, UKF, and particle filters are all special cases — so interviewers probe it to gauge whether you understand state estimation from first principles rather than as black-box library calls. Expect to derive the belief recursion from the Markov assumption, explain the motion (prediction) and measurement (correction) models, and reason about why the update step is a Bayesian posterior. Fluency here signals you can debug real localization and sensor-fusion stacks, not just call a filter.
Application focus
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At a glance
The recursive Bayes filter: each cycle predicts the belief forward with the motion model, then corrects it with the latest measurement, and the result feeds back as the prior for the next step.
What to study
- ✓The recursive belief equation: derive bel(x_t) from the prior, motion model p(x_t | u_t, x_{t-1}), and measurement model p(z_t | x_t)
- ✓The two-step predict (control update / total probability) then update (measurement / Bayes rule with normalizer eta) loop
- ✓The Markov assumption and conditional independence that make the recursion tractable
- ✓How the general Bayes filter specializes into the Kalman filter, EKF, histogram filter, and particle filter
Study by time budget
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