Absolute continuity gives a positive real density μ-almost everywhere
AutoSamplingTheory.TechnicalLemmas.InformationTheory.RNLogRatio.density_ae_pos_of_absolutelyContinuous · theorem · Teaching coverage
Statement
Let μ and π be measures on a measurable space α. Assume μ is σ-finite, a Lebesgue decomposition of μ relative to π exists, and μ≪π. Then the selected real density ρ is strictly positive at μ-almost every point. The assertion is neither everywhere nor necessarily π-almost everywhere.
All objects and hypotheses
- α is any measurable space; no topology, standard-Borel, countability, or nonempty-space hypothesis is imposed.
- μ and π are arbitrary measures on α unless further conditions are stated; no probability or finite-mass assumption is implicit.
- Write a(x)=μ.rnDeriv π x∈[0,∞], ρ(x)=a(x).toReal, and r=RNLogRatio.logRatio μ π=MeasureTheory.llr μ π=Real.log ρ. All names refer to the same selected functions for the original pair (μ,π).
- μ is σ-finite; HaveLebesgueDecomposition μ π is supplied; and hμΠ proves μ≪π. No σ-finiteness of π or probability normalization is required.
Mathematical proof
1. Obtain positive extended RN values under μ
The decomposition and absolute continuity imply that a is positive outside a μ-null set.
Corresponding Lean step
MeasureTheory.Measure.rnDeriv_pos hmuPi
2. Transport a.e. finiteness from π to μ
σ-finiteness of μ gives a<∞ π-a.e. Absolute continuity transfers that assertion to μ-a.e.; it is not necessary to assume π σ-finite.
Corresponding Lean step
MeasureTheory.Measure.rnDeriv_lt_top; MeasureTheory.Measure.AbsolutelyContinuous.ae_le
3. Convert positive finite values
Intersect the two μ-a.e. sets. A nonzero extended value distinct from infinity has strictly positive real conversion.
Corresponding Lean step
filter_upwards; ENNReal.toReal_pos
Lean statement · density_ae_pos_of_absolutelyContinuous
The a.e. filter is μ. The decomposition instance and μ's σ-finiteness have separate roles: RN positivity and exclusion of infinity before real conversion.
Braces mark parameters Lean can infer; square brackets request structures such as a measurable space or probability measure. Named hypotheses are mathematical premises, not facts established by this declaration. Section parameters are described in the mathematical hypotheses above; the module link retains their exact source context.
theorem density_ae_pos_of_absolutelyContinuous
(mu pi : Measure α) [SigmaFinite mu]
[Measure.HaveLebesgueDecomposition mu pi]
(hmuPi : mu ≪ pi) :
∀ᵐ x ∂mu, 0 < density mu pi xLean proof · density_ae_pos_of_absolutelyContinuous
The proof combines μ-a.e. positivity with π-a.e. finiteness transported through absolute continuity. The final conversion theorem needs both nonzero and non-infinite RN values.
Braces mark parameters Lean can infer; square brackets request structures such as a measurable space or probability measure. Named hypotheses are mathematical premises, not facts established by this declaration. Section parameters are described in the mathematical hypotheses above; the module link retains their exact source context.
theorem density_ae_pos_of_absolutelyContinuous
(mu pi : Measure α) [SigmaFinite mu]
[Measure.HaveLebesgueDecomposition mu pi]
(hmuPi : mu ≪ pi) :
∀ᵐ x ∂mu, 0 < density mu pi x := by
filter_upwards [Measure.rnDeriv_pos hmuPi,
hmuPi.ae_le (Measure.rnDeriv_lt_top mu pi)] with x hxPos hxTop
exact ENNReal.toReal_pos hxPos.ne' hxTop.ne
/-- Exponentiating the canonical log-density ratio recovers the canonical RN
density `mu`-a.e. under absolute continuity. -/Scope and omitted-condition boundaries
- Neither pointwise positivity nor π-a.e. positivity is asserted. In particular π may assign positive mass to a region where μ has zero density.
- An equality a.e. of function values does not by itself identify classical gradients. No pointwise choice on exceptional null sets or independence under arbitrary null-set changes is proved.
- Positivity μ-a.e. does not provide differentiability, finite entropy, or a score domain.
Source and reuse
ASTIS parents called
Mathlib API called (external library)
- MeasureTheory.Measure.rnDeriv_pos
- MeasureTheory.Measure.rnDeriv_lt_top
- MeasureTheory.Measure.AbsolutelyContinuous.ae_le
- ENNReal.toReal_pos
Mathematical sources
- Exact existing ASTIS declaration and body — Local source authority; renderer extracts the exact code.
- Extended RN positivity — Requires HaveLebesgueDecomposition and absolute continuity; conclusion is μ-a.e.
- RN finiteness — Requires SigmaFinite μ; initial conclusion is π-a.e.
- Existing declaration-visibility check — Read-only #check evidence; no build/test run.
ASTIS prose is not a quotation or a source-equivalence certificate. Definitions and aliases are explained as constructions, not counted as new mathematical proofs.