Reject non-finite linear-Gaussian primitive inputs

[FlorianPfaff]

Summary

• reject NaN and infinite vectors/matrices at the backend-native linear-Gaussian primitive boundary
• cover prediction and update inputs, including deterministic system inputs and noise covariances
• add regressions proving failed Kalman predict/update calls leave the filter state unchanged

Bug

pyrecest.filters._linear_gaussian rejected boolean and complex inputs but still accepted NaN/Inf. Those values could propagate through linear_gaussian_predict() or linear_gaussian_update() and then be installed into a KalmanFilter state with check_validity=False, silently poisoning subsequent estimates.

The higher-level linear-Gaussian model layer already rejects non-finite values, so the primitive API had inconsistent validation depending on which entry point was used.

Fix

Add a backend-to-NumPy finiteness check after vector/matrix coercion and before any prediction, innovation, solve, or state mutation.

Tests

Added regressions for non-finite:

• state means and covariances
• transition and measurement matrices
• process and measurement noise covariances
• deterministic system inputs and measurements
• Kalman prediction/update atomicity on validation failure

Branch is 2 commits ahead of main, 0 behind; the diff is limited to 11 source-line additions plus the focused test module.

[github-actions]

✅MegaLinter analysis: Success

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✅ REPOSITORY	secretlint	yes		no		no	108.78s
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