How statement and proof provenance work
The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.
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- AI-generated: a genuinely novel statement formulated by AI, with no source for the claim itself.
These labels describe origin, not correctness: citations and verification chips remain separate evidence.
Conditional entropy chain and disjoint-block bounds — Examples
1 · Prerequisites
- Absolute and Conditional Convergence; Rearrangement; Products
- Binary Operations, Monoids, Groups and Subgroups
- Compactness in Metric Spaces
- Complete Metrizability, Čech-Completeness, and Baire Category
- Completeness, Completion, and Uniform Continuity
- Conditional Distributions and Regular Conditional Probability
- Conditional entropy chain and disjoint-block bounds
- Conditional Expectation
- Construction of the Natural Numbers
- Construction of the Real Numbers via Cauchy Sequences
- Construction of the Real Numbers via Dedekind Cuts
- Continuity, IVT, EVT, and Uniform Continuity
- Cosets, Index and Lagrange's Theorem
- Countability and Uncountability
- Countability Axioms and Cardinal Functions
- Divisibility, Euclidean Domains, Principal Ideal Domains and Unique Factorisation
- Finite Counting, Factorials and Binomial Coefficients
- Foundations of the Real Numbers for Analysis
- Ideals, Quotient Rings and the Isomorphism Theorems for Rings
- Infinite Product Measures and Kolmogorov Extension
- Limits of Real Functions
- limsup, liminf, and Subsequential Limits
- Measurable Functions and Simple Approximation
- Measures and Their Basic Properties
- Metric Spaces
- Monotone Functions, Discontinuities, and Continuity Sets
- Monotone Sequences, Bolzano-Weierstrass, and Cauchy Completeness
- Normal Subgroups and Quotient Groups
- Order, Zorn's Lemma, and the Axiom of Choice
- Polynomial Rings, the Division Algorithm and Roots
- Power Series and Real-Analytic Functions
- Probability Spaces Random Variables and Expectation
- Product Measures and the Fubini Tonelli Theorems
- Properties of the Integral and the Working FTC
- Relations, Functions, and Quotients
- Rings, Subrings, Integral Domains and Fields
- Roots, Rational Powers, and Classical Inequalities
- Sequences and Limits
- Sequences and Series of Functions; Uniform Convergence
- Series: Convergence and the Nonnegative Tests
- Sigma Algebras and Borel Sets
- Signed and Complex Measures Hahn and Jordan
- Simple Field Extensions and the Construction of the Complex Numbers
- Spatial Measure And Information
- Suprema and Infima
- The Derivative and the Mean Value Theorems
- The Exponential Function
- The Lebesgue Integral and the Convergence Theorems
- The Logarithm and General Powers
- The Radon Nikodym Theorem and Lebesgue Decomposition
- The Riemann Integral: Definition and Integrability
- The ZFC Axioms and the Basic Set Constructions
- Topological Spaces and Continuity
- Topology of ℝ
- Vector Spaces, Linear Subspaces, Span and Direct Sums
2 · Summary
These finite Bernoulli countermodels separate energy-density or relative-entropy density agreement from convergence at a selected coordinate. They compute the first-coordinate total-variation discrepancy and the conditional relative entropy exactly, then verify concentration and entropy-density limits directly. They are mathematical counterexamples, not experimental observations or claims about a physical ensemble.
3 · Logical flowchart
4 · Definitions, postulates, theorems and proofs
None yet.
5 · Examples, experiments and counterexamples
Energy concentration without local equivalence
Statement refuted
Fix and . For every integer , let be the product probability on , where each coordinate equals with probability , and define . The refuted claim is: every sequence of probability laws on for which there are deterministic numbers with has first-coordinate marginals converging in event-supremum total variation to . Use . This is a finite-model claim, not an experimental assertion.
Facts & Assumptions
Given: 0<θ<1, ε>0, the Bernoulli product reference, and the configurations above.
Counterexample
Given: , , the independent Bernoulli- reference , and .
Let have its first coordinates equal to one and the rest zero, and set . Then , since . For all sufficiently large , the first coordinate of is one.
The event in the first-coordinate space has probability difference , so for every sufficiently large . In fact equality holds because on a two-point space the event-supremum difference between and Bernoulli- is . Together with the deterministic energy limit in step 1.1, this refutes the stated implication.
Small entropy density without homogeneous local convergence
Statement refuted
Assume AC. Fix and , and let be the reference and energy of Energy concentration without local equivalence. The refuted implication is: for every sequence of laws , if , in -probability, and , then the first-coordinate marginal of converges in event-supremum total variation to Bernoulli. Here and use natural logarithms.
Facts & Assumptions
Given: AC, the Bernoulli model and parameters above.
AC is the choice hypothesis of the conditional entropy chain supplier (The Axiom of Choice).
Conditional entropy chain and disjoint-block bounds: the finite product KL chain rule separates the first coordinate from its conditional remainder.
Counterexample
Given: The finite product laws and energy.
Set , using the empty product for . Under , surely and the remaining coordinates retain their independent Bernoulli- law. The likelihood is on and zero elsewhere, so integration gives . Equivalently, [F1] decomposes the divergence into and a zero conditional remainder. Dividing by gives vanishing entropy density.
Under , with binomial . Since is the sum of independent Bernoulli coordinates, and , by expanding the mean and variance and using zero cross-covariances. Thus and . For any , ; integrating gives the Chebyshev bound and convergence in probability. Its Shannon entropy is by adding the logarithms of the independent-coordinate probabilities, so , where . Yet the first coordinate remains one and is at total-variation distance from Bernoulli. This proves all claims and refutes the statement.