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 high ELBO alone does not certify posterior calibration
Remark
The numerical value of an ELBO depends on the scale of the unnormalized target. It alone is not a bound on posterior discrepancy, calibration, or downstream error. Here a calibration guarantee means a separately proved property of repeated-sampling coverage or posterior-probability calibration; it is not defined by the ELBO. Under AC, the library KL identity controls the gap through the reverse divergence, when that gap is known.
Facts & Assumptions
Given: A measurable with and an admissible for which differs from . The rescaling argument is choice-free; assume AC only for the library KL interpretation in the final step.
Variational family and evidence lower bound: admissibility requires support on and finite .
Variational family and evidence lower bound: for an admissible .
Evidence lower bound is log evidence minus reverse KL: under its hypotheses and AC, .
Monotonicity and nonnegative homogeneity of the nonnegative integral: multiplying a nonnegative measurable function by a positive finite scalar multiplies its integral by the same scalar.
Order, continuity, range, and the product, quotient, and reciprocal laws for the natural logarithm: is onto and satisfies for positive .
The Axiom of Choice: AC is assumed for the library KL interpretation only.
Verification
Given: The hypotheses in the Statement.
Fix any . By the onto clause of [F5], choose with . Define . Homogeneity [F4] gives , so and both and remain unchanged. Since , the support condition in [F1] is unchanged. On the q-support, , so the logarithmic integrability condition remains finite.
On the q-support the logarithm product law [F5] gives . Using [F2], . Since is arbitrary, the absolute ELBO can be made arbitrarily large while , , and any fixed discrepancy between them stay unchanged.
Under [A1], the decomposition [F3] gives . Without control of , a separate calibration theorem, or another posterior-discrepancy bound, the absolute ELBO alone supplies no calibration guarantee.
Depends on
Used by
Nothing in the library uses this result yet.
Dependency tree · two levels
28 results within two dependency steps of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.
Sources
- David M. Blei, Alp Kucukelbir, and Jon D. McAuliffe, Variational Inference: A Review for Statisticians (2017) (standard reference, not scraped)
- Michael I. Jordan, Zoubin Ghahramani, Tommi S. Jaakkola, and Lawrence K. Saul, An Introduction to Variational Methods for Graphical Models (1999) (standard reference, not scraped)