Fixed-support Fisher gap
Abstract
A normalized probability curve on one fixed finite support has a strict Fisher-information gap.
Let ι be any finite index type. The same support values x(j) and the same nonnegative weight curve p(j,u) are used for every parameter u in the open interval (2a−1,1). The curve is normalized, has mean a, and has second moment (1+u)/2 throughout that interval. The Fisher sum is filtered to the indices with positive current weight; indices of weight zero contribute zero because nonnegative differentiable weights have zero derivative at a local minimum.
Theorem 1.1 (The exact gap).
Proof. Machine-checked in Lean as D5/S3/Quantum/Information/FixedSupportFisherGap.result (✓ std3). ∎
Source. Repository-derived.
Commentary.
Assume 0<a<1, every support point lies in [−1,1], every weight is differentiable on (2a−1,1), and the displayed normalization and two moment identities hold for the same curve at every parameter. If its actual positive-support Fisher sum is at most R(1−a²)/((1−u)(1+u−2a²)) pointwise, then R is at least 1+a² divided by (1+4a+2(1+a) log 2)². The conclusion is uniform over the arbitrary finite fixed support and does not assume endpoint continuity or a limiting value of p.
The proof differentiates the three same-curve constraints, applies the finite weighted Cauchy–Schwarz inequality with zero-weight terms removed, and controls the resulting normalized mean along the interval. Monotonicity and continuity of the auxiliary expressions at the left endpoint produce the exact log 2 constant.
For nonvacuity, the support [−1,1/2,1] with a=1/2 and R=4/3 and weights ((1+2u)/12, 2(1−u)/3, (1+2u)/4) satisfies all hypotheses on (0,1); all three weights are strictly positive there, so the filtered Fisher sum is the full three-term sum.
References
- Truth anchor:
D5/S3/Quantum/Information/FixedSupportFisherGap.result