Bayesian Shannon-code evidence rate
OpenQAlgorithms.Papers.Independent.Intakethird100.P80.compression_limit85Fix the quantum experiment, setting law, iid trials, and continuous uniformly positive normalized local-theory prior of the Bayesian limit. Let be the actual Bayesian marginal likelihood. On its positive-probability support, use the Shannon binary code obtained by sorting words by decreasing probability, breaking ties by a fixed finite enumeration, and taking the first binary digits of the cumulative probability. The resulting words form a prefix code and have lengths satisfying
The iid law is a probability measure, the prior is integrable, and is finite. Almost surely every observed word has positive quantum and Bayesian likelihood, and
This gives the information-theoretic evidence rate (53), (85) for an actual support-restricted code; impossible words are excluded from its coding alphabet.
import Definitions.Def_QAlgorithms_Papers_Independent_Intakethird100_P80
noncomputable section
namespace QAlgorithms.Papers.Independent.Intakethird100.P80
open scoped BigOperators ENNReal Topology Classical
open MeasureTheory Filter
section ProbabilityCoding
/-- PDF pp.27,29,31, (53),(85): the explicit support-restricted Bayesian/Shannon code.
Its words have the source's ceiling lengths, and its excess over ideal Q length has the KL rate.
This is normalized convergence, not the invalid pathwise O(log n) claim in (46). -/
theorem compression_limit85 {S : BellExperiment} (Q : Table S) (hQ : NonlocalityProof Q)
(s : Sigma S) (w : (Potential S → ℝ) → ℝ) (hw : AdmissiblePrior w) :
Integrable w (simplexVolume S) ∧ iidLaw Q s.1 Set.univ = 1 ∧ lowerValue Q s.1 < ⊤ ∧
(∀ n, IsPrefixCode (fun z : {z : Fin n → BellTrial S // 0 < bayesMarginal s.1 w z} =>
shannonWord (fun t : Fin n → BellTrial S => bayesMarginal s.1 w t) z.1)) ∧
(∀ n (z : Fin n → BellTrial S), 0 < bayesMarginal s.1 w z →
let L := ((shannonWord (fun t : Fin n → BellTrial S => bayesMarginal s.1 w t) z).length : ℝ);
-Real.logb 2 (bayesMarginal s.1 w z) ≤ L ∧ L < -Real.logb 2 (bayesMarginal s.1 w z) + 1) ∧
∀ᵐ z ∂iidLaw Q s.1,
(∀ n, 0 < sampleLikelihood (trialJointLaw Q s.1) (initialTrials z n) ∧
0 < bayesMarginal s.1 w (initialTrials z n)) ∧
Tendsto (fun n : ℕ =>
(((shannonWord (fun t : Fin n → BellTrial S => bayesMarginal s.1 w t)
(initialTrials z n)).length : ℝ) + Real.logb 2 (sampleLikelihood (trialJointLaw Q s.1) (initialTrials z n))) / n)
atTop (𝓝 (lowerValue Q s.1).toReal) := by sorry
end ProbabilityCoding
end QAlgorithms.Papers.Independent.Intakethird100.P80
end
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What the Lean code literally says, in plain math · gpt-6.1-sol
Let consist of a positive finite number of parties, positive finite numbers of settings for each party, and positive finite numbers of outcomes at each party and setting. Write for its joint settings, for its joint outcomes at setting , for the finite set of assignments prescribing an outcome at every setting of every party, and for the prescribed joint outcome at . Let be a real table such that each is nonnegative and sums to ; for every subset of parties, settings agreeing on , and , the sum of over for all equals the corresponding sum of over ; and there exist positive local dimensions , a positive semidefinite joint matrix of trace , and positive semidefinite local matrices , summing to the identity over , such that , with . Let be any nonnegative distribution of total mass on , including distributions with zero coordinates. For a distribution , define , and define trial laws on by and . Define if some trial has and , and otherwise define it as the extended nonnegative real corresponding to ; put . Let be Hausdorff measure of dimension , restricted to , and let be continuous on that simplex, satisfy throughout it for some real , and have . For each length- trial sample , put and . Using the finite enumeration supplied on the length- sample space, define
and let be the Boolean word of length whose entry at index is true exactly when ; natural-number floor and ceiling are truncated at zero, and the total logarithm has . Let be the infinite product of the trial measure . The conclusion is that is integrable with respect to , has total mass , , and, for every , restricted to samples with is prefix-free, meaning that a prefix relation between two such words implies equality of their samples. Moreover, for every and every sample with ,
Finally, for -almost every infinite sequence ,
where and is the real value of the finite number . All universal claims about include : its likelihood and marginal are , its word is empty, and the quotient's term at is under total real division.
Confirmed by the mission captain (proposal self-audit).
Confirmed by the moderator at approval.