Stabilize discrete-state prior normalization

[FlorianPfaff]

Summary

• normalize finite discrete-state prior vectors after scaling by their maximum entry
• prevent overflow in the probability sum while preserving relative mass
• add a public forward/backward regression with extreme finite initial probabilities

Bug

The compatibility-layer probability validator normalized priors with values / values.sum(). A valid vector can contain individually finite values whose sum overflows to infinity. For example, [finfo.max, finfo.max / 2] has a mathematically well-defined normalized ratio of 2:1, but the direct floating-point sum is inf, so the implementation returned [0, 0]. The forward recursion then reported that the first emission had no prior-supported likelihood mass.

Fix

Scale the non-negative vector by its largest entry before summing. All scaled entries are in [0, 1], the relative probabilities are unchanged, and the positive-mass check remains explicit.

Validation

• reproduced the pre-fix overflow and zero-vector normalization with NumPy
• verified scale-first normalization yields [2/3, 1/3]
• added tests/filters/test_discrete_state_probability_overflow.py
• branch comparison against main: two commits ahead, zero behind; 7 production-line changes and one focused regression file

The full repository test matrix is delegated to GitHub Actions.

[github-actions]

✅MegaLinter analysis: Success

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✅ REPOSITORY	betterleaks	yes		no	no	2.26s
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[FlorianPfaff]

Closing as superseded by #4405, the newer current-main implementation of stable discrete-state prior normalization.