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.
Credible sets and frequentist coverage — Examples and Counterexamples
1 · Prerequisites
- Absolute and Conditional Convergence; Rearrangement; Products
- Adaptive empirical-Bayes credible-set calibration and failure
- Areas of Elementary Plane Figures
- Binary Operations, Monoids, Groups and Subgroups
- Brownian Motion Construction and Continuity
- Central Limit Theorems
- Compactness
- Compactness in Metric Spaces
- Completeness, Completion, and Uniform Continuity
- Conditional Distributions and Regular Conditional Probability
- Conditional Expectation
- Confidence Sets and Inversion
- 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
- Convergence: Nets and Filters
- Cosets, Index and Lagrange's Theorem
- Countability and Uncountability
- Credible sets and frequentist coverage
- Detection and quantification limits
- Determinants of Matrices over a Commutative Ring
- Divisibility, Euclidean Domains, Principal Ideal Domains and Unique Factorisation
- Filters and Ultrafilters
- Finite Counting, Factorials and Binomial Coefficients
- Finite Probability and the Probabilistic Method
- Foundations of the Real Numbers for Analysis
- Fubini and Change of Variables
- Fundamental Trigonometric Identities
- Gaussian Elimination, Elementary Matrices and Reduced Row Echelon Form
- Group Actions, Orbits, Stabilisers and Cayley's Theorem
- Ideals, Quotient Rings and the Isomorphism Theorems for Rings
- Improper and Parameter-Dependent Multiple Integrals
- Improper Integrals
- 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
- Matrices, the Matrix of a Linear Map, and Change of Basis
- 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
- Orthonormal Bases, Parseval and Fourier Series
- Outer Measure and the Caratheodory Extension Theorem
- Polynomial Rings, the Division Algorithm and Roots
- Posterior Prediction, Credible Sets, and Bayes Actions
- 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
- Sequences and Limits
- Sequences and Series of Functions; Uniform Convergence
- Series: Convergence and the Nonnegative Tests
- Sigma Algebras and Borel Sets
- Simple Field Extensions and the Construction of the Complex Numbers
- Sine, Cosine, and the Definition of Pi
- Statistical Experiments, Models and Identifiability
- Subspaces, Products, and Quotients
- Suprema and Infima
- Symmetric Groups, Cycle Decomposition and the Sign Homomorphism
- The Complex Exponential and Euler's Formula
- The Derivative and the Mean Value Theorems
- The Exponential Function
- The Inverse and Implicit Function Theorems
- The Lebesgue and Riemann Integrals Compared
- The Lebesgue Integral and the Convergence Theorems
- The Logarithm and General Powers
- The Lᵖ Spaces Holder Minkowski and Riesz Fischer
- The Riemann Integral in Rᵐ and Jordan Content
- The Riemann Integral: Definition and Integrability
- The Topology of Euclidean Space
- The Total Derivative in ℝᵐ → ℝⁿ
- The ZFC Axioms and the Basic Set Constructions
- Topological Spaces and Continuity
- Topology of ℝ
- Vector Spaces, Linear Subspaces, Span and Direct Sums
- Weak Convergence Tightness and Representation
2 · Logical flowchart
3 · Definitions, theorems and proofs
None yet.
4 · Examples, counterexamples and false statements
A proper Gaussian prior yields a credible interval with undercoverage
Example
Assume AC. Observe with fixed truth , , and proper prior . Let and , let be the unique number satisfying , and put . The posterior is , so the central interval has posterior mass exactly for every . At the fixed truth , its frequentist coverage is .
Facts & Assumptions
Given: AC and the one-dimensional normal likelihood and proper normal prior in the Example, with and .
AC states that every family of nonempty sets has a choice function. The Axiom of Choice
Posterior credibility and frequentist coverage: posterior mass is computed under the data-indexed posterior kernel, while frequentist coverage is the fixed-truth sampling probability of set membership.
Standard normal and normal laws: has a positive symmetric standard normal density, and is its affine image under .
If is continuous on a closed interval , then every value between and equals for some . Intermediate value theorem, by bisection with a canonical left-half rule: a continuous function on takes every value between and
Verification
Given: The hypotheses in the Example and Facts & Assumptions.
Under [A1], the standard normal probability law of [F2] is supplied. Its density is positive and symmetric and integrates to one, so is continuous and strictly increasing, with and as . Choose with . By [F3] on , there is a unique with .
The prior density times likelihood is proportional to times a factor independent of . Normalization gives posterior law .
Under the fixed truth , write . The event is equivalent to , hence to .
The interval membership graph is , the inverse image of the closed interval under a continuous map. It is closed, hence Borel, so the sampling membership event is measurable.
Under that posterior, is standard normal. The interval radius therefore gives posterior mass for every observed .
Since the standard normal density is positive and symmetric, . Thus the interval has posterior mass but frequentist coverage below at .
Remarks
A separate one-observation Bernoulli example with a uniform prior has zero coverage at the truth ; that conclusion is specific to its Bernoulli likelihood and prior. The Gaussian calculation above uses a different proper prior and sampling law.
Sparse-gap truths defeat uniform coverage over a hyperrectangle
Statement refuted
Assume AC. Fix , , , finite , , and . For , observe in the Gaussian sequence experiment, where is fixed, , and the are independent . Define . For , use the product prior and its product Gaussian posterior with coordinate mean and variance . Select as a Borel global maximizer over of Assume the score series is finite on a Borel full-measure data domain for the truths considered and is upper semicontinuous in there. For , let be the posterior quantile of , and let be the ball centered at with radius . Set if . There exists a finite fixed for which
Statement references
Facts & Assumptions
Given: AC and the model, prior, selector, in the Statement refuted.
AC supplies the countable Gaussian product construction used by the empirical-Bayes procedure. The Axiom of Choice
only if for every integer . Polished-tail class for a Gaussian sequence
For the sparse-gap construction below, every deterministic nonnegative inflation sequence satisfying eventually, for some finite , has . Sparse-gap truth causes empirical-Bayes credible-set undercoverage
For each fixed , there is a finite inflation constant for which coverage tends to one uniformly over with . Conservative coverage of adaptive empirical-Bayes credible balls on polished tails
For , the procedure uses the posterior-mean-centered ball with radius times the posterior quantile; its zero-selector branch is the whole space. Empirical-Bayes credible-ball procedure in the inverse Gaussian sequence model
At a fixed truth, frequentist coverage is the sampling probability of the membership event . Posterior credibility and frequentist coverage
Counterexample
Put , , , and . Set and define the fixed truth by For every fixed , this truth belongs to , has , is outside every fixed polished-tail class, and has subsequence coverage tending to zero.
Given: The hypotheses in the Statement refuted and the construction above.
Every squared coordinate is either zero or , so and . Also and , hence and the first zero gap contains . Thus and .
Fix any polished-tail parameters , integer , and . The recursion gives , so every earlier gap ends below . Also ; because and , for all sufficiently large we have and , whence . Since , the block lies in the current zero gap . For all sufficiently large , , so is before the next first gap; applying the same separation at later indices puts all still later gaps farther right. Since eventually, ; earlier gaps end below . Hence lies outside every zero gap and is in the tail from , with . The block sum is zero while that tail is positive, violating [F2]. Thus is outside every fixed polished-tail class.
For a fixed , take and . Since and , we have for every . Applying [F3] gives . By [F6] this is the frequentist coverage at the fixed truth; because by step 1.1, the infimum coverage over that hyperrectangle cannot have lower limit at least .
The positive coverage statement [F4] has the additional polished-tail hypothesis, which fails for by step 2.1. Thus the sparse-gap undercoverage does not contradict the polished-tail theorem; it refutes only the broader fixed-inflation claim over the full hyperrectangle.