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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These labels describe origin, not correctness: citations and verification chips remain separate evidence.
Spatial Measure And Information — Examples
1 · Prerequisites
- Absolute and Conditional Convergence; Rearrangement; Products
- Areas of Elementary Plane Figures
- Binary Operations, Monoids, Groups and Subgroups
- Compactness
- Compactness in Metric Spaces
- Complete Metrizability, Čech-Completeness, and Baire Category
- Completeness, Completion, and Uniform Continuity
- Conditional Distributions and Regular Conditional Probability
- 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
- Filters and Ultrafilters
- 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
- Lebesgue Measure on Euclidean Space
- Limits of Real Functions
- limsup, liminf, and Subsequential Limits
- Linear Independence, Bases and Dimension
- Linear Transformations, Rank-Nullity and Quotient Spaces
- 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
- Outer Measure and the Caratheodory Extension Theorem
- 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
- Rⁿ as a Normed Space; Vector-Valued Functions
- Roots, Rational Powers, and Classical Inequalities
- Separation Axioms: the Hierarchy
- 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
- Standard-Borel Real Codings and Determining Classes
- Subspaces, Products, and Quotients
- Suprema and Infima
- The Cantor Set, Baire Category, and Measure Zero in ℝ
- 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 in Rᵐ and Jordan Content
- The Riemann Integral: Definition and Integrability
- The Topology of Euclidean Space
- 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 examples verify the zero-information reference baseline, compute the specific relative entropy of a Poisson law with changed intensity, and exhibit a local superlinear count observable excluded from the local-tame test class. They use only the mathematical configuration and information definitions on the A page; no physical preparation or observation is asserted. The B inventory is 3 items (cap 8).
3 · Logical flowchart
4 · Definitions, postulates, theorems and proofs
None yet.
5 · Examples, experiments and counterexamples
The Reference Has Zero Specific Relative Entropy
Example
Take , the unit-intensity Poisson configuration reference. This gives the zero-information baseline for the A-page specific relative entropy.
Facts & Assumptions
Given: as probability laws on the configuration space.
Finite-region relative entropy is when (Relative Entropy of Probability Measures).
Specific relative entropy is the large-cube limit of normalized finite-region relative entropies (Specific Relative Entropy Density).
Poisson Reference Counts and Translation Invariance: under the declared AC hypothesis, is stationary and has expected unit-cell count one, so it belongs to the stationary finite-intensity domain of [F2].
Verification
Given: .
By [F3], satisfies the stationary finite-intensity hypotheses of [F2]. For every bounded cube , equality of the laws gives and the Radon–Nikodym derivative -almost everywhere. Thus .
Every normalized cube entropy is therefore zero, so its limit is by [F2]. Also -almost surely for every such .
Specific Relative Entropy of a Poisson Law with Changed Intensity
Example
For intensity , let be the translation-invariant Poisson law from Poisson Configuration Reference, compared with the unit-intensity reference . Its specific relative entropy is The parameter is a mathematical intensity relative to the chosen reference; no physical preparation is asserted.
Facts & Assumptions
Given: AC, , , and with reference .
The law is invariant under every translation, its cell count is Poisson with mean , conditional positions have the same uniform law for all , and disjoint cells are independent (Poisson Configuration Reference, Poisson Reference Counts and Translation Invariance).
Finite-region relative entropy is computed from a Radon–Nikodym density, and the specific entropy is the normalized cube limit (Relative Entropy of Probability Measures, Specific Relative Entropy Density).
The class consists exactly of laws invariant under every with finite . Such laws integrate every local tame observable (Local-Tame Topology).
Verification
Given: The hypotheses and the laws in Facts & Assumptions.
In one unit cell, the conditional distribution of point positions given the count is identical for and . The ratio of their count probabilities at is Thus this is the Radon–Nikodym density on that cell.
The cube is the disjoint union of unit cells. Independence in [F1] makes the finite-cube density the product of the cell densities:
The count is Poisson with mean by [F1]. Directly from its mass function and the exponential series, Therefore [F2] gives for every positive integer .
By [F1], is invariant under every translation, and step 3.1 with gives . Thus by [F4], which verifies the domain hypothesis of the specific-entropy theorem in [F2]. Its integer-cube supremum formula applied to step 3.1 yields and identifies this value with the real-side cube limit. At the value is zero, the reference-law baseline.
A Superlinear Count Test Is Not Local-Tame
Example
For the unit cell , the observable is local but is not one of the tests defining the local-tame topology.
Facts & Assumptions
Given: The unit-cell configuration space and the definition of local-tame observables.
Every cell configuration is finite, and is measurable with respect to the restriction to (Poisson Configuration Reference).
A local-tame observable supported in must satisfy for one finite constant (Local-Tame Topology).
Verification
Given: .
By [F1], is finite and measurable from the restriction to , so its square is a measurable local observable.
Fix any finite and choose an integer . The cell configuration space contains configurations with exactly distinct points in . On each such configuration, , so the tame growth bound in [F2] fails for this . Since was arbitrary, is not tame.