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.
- Literature-sourced: the exact statement appears in a cited source; only wording and notation differ.
- AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
- 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.
Method of Types and Finite Large Deviations — Examples
1 · Prerequisites
- Absolute and Conditional Convergence; Rearrangement; Products
- Binary Operations, Monoids, Groups and Subgroups
- Compactness in Metric Spaces
- Completeness, Completion, and Uniform Continuity
- Concentration
- 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
- Divisibility, Euclidean Domains, Principal Ideal Domains and Unique Factorisation
- Finite Counting, Factorials and Binomial Coefficients
- Finite Probability Spaces and Random Variables
- Foundations of the Real Numbers for Analysis
- Ideals, Quotient Rings and the Isomorphism Theorems for Rings
- Independence Borel Cantelli and Zero One Laws
- Limits of Real Functions
- limsup, liminf, and Subsequential Limits
- Martingale Inequalities and Convergence
- Measurable Functions and Simple Approximation
- Measures and Their Basic Properties
- Method of Types and Finite Large Deviations
- 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
- Pointwise Extended-Real Function Products
- Polynomial Rings, the Division Algorithm and Roots
- Power Series and Real-Analytic Functions
- Probability Spaces Random Variables and Expectation
- Properties of the Integral and the Working FTC
- Quantum Communication and Fingerprinting
- 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
- 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 Lᵖ Spaces Holder Minkowski and Riesz Fischer
- 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
- Triangularisation, Generalised Eigenspaces and Jordan Canonical Form
- Vector Spaces, Linear Subspaces, Span and Direct Sums
2 · Summary
This companion computes binary type-class sizes with their polynomial factors visible, then works a finite Bernoulli upper-tail event and its asymptotic relative-entropy rate. The Bernoulli calculation includes the threshold ceiling, the all-ones endpoint, and a one-sided Hoeffding comparison under its stated Axiom of Choice assumption. The final counterexample shows why an entropy exponential alone is not an exact finite type-class count.
3 · Logical flowchart
4 · Definitions, theorems and proofs
None yet.
5 · Examples, counterexamples and false statements
Binary type classes at length ten
Example
For and , the types with and have class sizes and . Their entropy exponentials are respectively and , and the polynomially corrected bounds use the factor .
Facts & Assumptions
Given: The binary alphabet , block length , and the two stated attainable types.
Empirical types, n-types, and type classes: a type class consists of the words with prescribed symbol counts.
Polynomially sharp bounds for type-class cardinality: for an alphabet of size , .
Verification
Given: The data above.
For , the counts are , so and .
For , the counts are , so and . Since and , we have , retaining the polynomial factor in [F3].
Since , , , and , cross-multiplication in step 1.1 gives , as [F3] states.
At endpoint types and , each type class is a singleton and , so the lower cardinality bound is , while the class size and upper bound equal ; thus [F3] also covers zero-coordinate boundary cases.
A finite Bernoulli large-deviation tail
Example
For , let be the binary probability vector , . On use the product law from Finite product mass on words, and let be its coordinate maps; by The finite product law has iid coordinate maps these are independent Bernoulli variables. Let , define , , and with zero-mass summands omitted, , and for . Then Consequently including the value when . For , , and , the exact tail is ; under AC, Hoeffding also gives the bound for this same event.
Facts & Assumptions
Given: A finite Bernoulli product model with parameter , threshold , and integer . The Hoeffding comparison assumes AC.
The Axiom of Choice: every family of nonempty sets has a choice function. The example invokes this axiom only to apply the Hoeffding supplier in step 4.1.
Empirical types, n-types, and type classes: the empirical type of a binary word is its fraction of ones, and its type class consists of words with that fraction.
Finite distributions, entropy, divergence, and stochastic matrices: finite relative entropy uses natural logarithms, , and equals if positive mass is placed where the reference law is zero.
Finite probability bounds for an empirical type: for each binary -type , .
Finite relative entropy is continuous on a simplex with positive reference law: when , is continuous on .
Finite-alphabet Sanov bounds on the support simplex: on the support simplex, the Sanov liminf uses the infimum over relative interior and the limsup uses the infimum over relative closure; equality of these infima gives the limit.
Hoeffding inequality for independent bounded sums: under AC, for independent integrable and , the one-sided bound is when the denominator is positive.
The mean value theorem, as the case of Cauchy's: for continuous on with and differentiable on there is with : if a real function is continuous on a closed interval and differentiable on its interior, its increment equals its derivative at some interior point times the interval length.
The type classes partition the word space and have polynomial count: on a binary alphabet there are at most attainable -types.
The finite product law has iid coordinate maps: the sample law on is a probability law and its coordinate variables are independent with Bernoulli marginal law.
Verification
Given: The stated Bernoulli model and thresholds.
If , every coordinate equals almost surely by [F10], so the tail is zero and ; both finite bounds have right-hand sides zero. If , the tail is the union of binary type classes with , and the attainable type belongs to the event, so [F3] gives the lower bound.
Suppose and . For every , [F7] gives . For any , the finite logarithmic formula is continuous on and differentiable on , so [F8] gives . Thus is increasing on , and continuity [F4] extends to .
For and , there are at most binary types by [F9]. Each tail type has probability at most by [F3], and step 1.2 gives , so summing the types proves the finite upper bound.
If , then for the relative interior in is and the closure is . By step 1.2 and continuity [F4], both divergence infima equal , so [F5] gives the stated limit. If and , the event is exactly all ones and has probability , giving the limit directly. The case is in step 1.1.
For and , summing the type upper bounds from step 2.1 yields the displayed finite upper bound; when and , step 2.2 gives the exact value , while is covered in step 1.1.
For , , and , the event contains four words with three ones and one with four, so its probability is . By [F10], the four coordinates are independent and take values in , hence are integrable. Thus [F6] applies with on the event and for bounds . Its exponent is , giving . This comparison uses [A1]; the finite type and Sanov calculations in steps 1.1–3.1 are choice-free.
A balanced binary type class is not exactly exponential in entropy
Statement refuted
For every finite alphabet, every , and every -type , the type-class size is exactly .
Facts & Assumptions
Given: The binary alphabet and block length .
Empirical types, n-types, and type classes: consists of the words whose empirical vector is .
Polynomially sharp bounds for type-class cardinality: for an alphabet of size .
Counterexample
Given: and the binary type .
The words with type are and , so .
By [F2], , and therefore . This single attainable type refutes the universal exact-equality claim.
At the boundary type , the only word is , and both and equal ; this boundary equality does not rescue the refuted universal claim and is consistent with [F3].