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.
A full-dimensional flow cannot represent a line-supported law exactly
Example
Assume the Axiom of Countable Choice. Let be the probability measure on with density relative to one-dimensional Lebesgue measure, let be , and define a measure on Borel sets by This probability measure is concentrated on the line segment and satisfies . It is not the output of any normalizing flow whose base measure has a Lebesgue density.
Example references
Facts & Assumptions
Given: Countable Choice, the interval probability measure , the continuous embedding , and its image measure .
The Axiom of Countable Choice, written , asserts that every sequence of nonempty sets has a choice function. The Axiom of Countable Choice ()
A nonnegative measurable density defines a measure by . The measure with density relative to
A flow from a Lebesgue density through a global diffeomorphism has density . A diffeomorphic flow has the inverse-Jacobian density
A continuous map has Borel preimages of Borel sets. A continuous map has Borel preimages of Borel sets
Every closed box in is Borel and Lebesgue measurable; its measure is , so has one-dimensional Lebesgue measure . A box in with parameters is Lebesgue measurable of measure , whichever of its faces are included
A rectangle with a degenerate side is Lebesgue null under Countable Choice. A box with a degenerate side is Lebesgue null, and so is every coordinate hyperplane in
A nonnegative measurable density integrates to zero over a measurable null set. A nonnegative integral over a null set vanishes
A normalizing flow is a finite composition of global diffeomorphisms between open equal-dimensional Euclidean domains; its output measure is obtained by taking preimages under the flow. A normalizing flow and its base density
Verification
Given: Countable Choice, the image measure of under , and an arbitrary normalizing flow with Lebesgue base density.
The function is continuous, so [F3] makes Borel for every Borel . For pairwise disjoint Borel sets , their preimages are pairwise disjoint and ; countable additivity of therefore makes a measure. By [F1] and [F4], , whence and .
The set is a closed box, hence Borel by [F4]. Its second side is degenerate, so [F5] gives .
By [F7] and [F2], the flow output has a nonnegative Lebesgue density on . Applying [F1] and [F6] to the null set gives .
The target measure satisfies by step 1.1, whereas every such flow output satisfies by step 1.3. Therefore , so no full-dimensional flow in the stated class represents this target law exactly.
Depends on
- A normalizing flow and its base density
- A diffeomorphic flow has the inverse-Jacobian density
- The Axiom of Countable Choice ($\mathrm{AC}_\omega$)
- The measure with density $f$ relative to $\mu$
- A box in $\mathbb{R}^n$ with parameters $a_i\le b_i$ is Lebesgue measurable of measure $\prod_{i<n}(b_i-a_i)$, whichever of its faces are included
- A continuous map has Borel preimages of Borel sets
- A box with a degenerate side is Lebesgue null, and so is every coordinate hyperplane in $\mathbb{R}^n$
- A nonnegative integral over a null set vanishes
Used by
Nothing in the library uses this result yet.
Dependency tree · two levels
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Sources
- George Papamakarios, Eric Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, and Balaji Lakshminarayanan, Normalizing Flows for Probabilistic Modeling and Inference, arXiv:1912.02762v1 (standard reference, not scraped)
- Dan Sheldon, COMPSCI 688: Probabilistic Graphical Models, Lecture 23: Normalizing Flows (annotated course notes) (standard reference, not scraped)