aod-proof-and-analysis-toolkit

You validate Bayesian occupancy probability calculations by auditing global prior periods, logit-space probability shifts, and statistical sufficiency to ensure learned probabilities have enough trusted data before merging a pull request.

To prevent timezone anomalies in occupancy detection statistics, audit time window calculations to ensure UTC and local timezone mixing does not corrupt ratio numerators, denominators, or decay factor formulas.

The best way to audit decay factor formulas in area occupancy detection code is to confirm the decay calculation matches the expected mathematical formula and verify the floor cutoff prevents indefinite probability decay.

Yes, validating probability math in occupancy detection code requires in-depth knowledge of Bayesian occupancy modeling and statistical analysis to accurately evaluate prior calculations, logit-space boosts, and statistical sufficiency.

Area-to-area couplings cause runaway feedback loops in occupancy modeling when learned probabilities and activity patterns recursively amplify each other without statistical sufficiency checks or ratio consistency boundaries.

To check if learned probabilities have enough data to be trusted, evaluate statistical sufficiency to determine if the collected activity patterns adequately support the estimated probability shifts and prior calculations.