Finite weighted sources and strong extraction #
Real weights describe finite distributions without requiring their atoms
to have equal probability. Normalization and the point-mass cap are separate
predicates. The cap K * p x ≤ 1 expresses min-entropy at least log₂ K
without logarithms or division in conditional-source arguments.
Distance is half the sum of absolute coordinate differences. The weighted seeded test samples the source according to its weights and an independent uniform seed, retaining both the seed and output in the test. All finite-sum operations remain defined on arbitrary real weights; probability theorems state normalization and nonnegativity explicitly.
Every point has probability at most the reciprocal of the support threshold.
Equations
- Algebraic.Cutwidth.Extractor.CappedWeight p K = ∀ (x : α), ↑K * p x ≤ 1
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The probability mass assigned to a finite test.
Equations
- Algebraic.Cutwidth.Extractor.weightTestProb p T = ∑ x ∈ T, p x
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Push weights through a deterministic map, counting every source occurrence.
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Uniform weights, with the zero convention on an empty sampling type.
Equations
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Test probability for a weighted source and an independent uniform seed.
Equations
- Algebraic.Cutwidth.Extractor.weightedSeededTestProb p E T = (∑ x : α, p x * ↑{y : Seed | (y, E x y) ∈ T}.card) / ↑(Fintype.card Seed)
Instances For
Strong extraction from every normalized source satisfying the point-mass cap.
Equations
- One or more equations did not get rendered due to their size.