ProbabilityTheory.HasCondSubexponentialMGF.cgf_le
Lemma
No docstring.
Types
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Ω : Type u_1m : MeasurableSpace ΩA measurable space is a space equipped with a σ-algebra.mΩ : MeasurableSpace ΩStandardBorelSpace ΩA standard Borel space is a measurable space arising as the Borel sets of some Polish topology.
Given
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μ : MeasureTheory.Measure ΩA measure is defined to be an outer measure that is countably additive on measurable sets, with the additional assumption that the outer measure is the canonical extension of the restricted measure.MeasureTheory.IsFiniteMeasure μA measureμis called finite ifμ univ < ∞. -
X : Ω → ℝ -
V : ℝ -
b : ℝ
Assuming
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hm : m ≤ mΩimplicit -
h : HasCondSubexponentialMGF m hm X V b μA random variableXhas a conditionally sub-exponential moment-generating function with parameters(V, b)with respect to a sigma-algebramand a measureμif for alltwithb * |t| ≤ 1,… -
hV : 0 ≤ V
Then
∀ᵐ (ω' : Ω) ∂μ.trim hm, ∀ (t : ℝ), b * |t| ≤ 1 → cgf X ((condExpKernel μ m) ω') t ≤ V * t ^ 2 / 2f.Eventually p or ∀ᶠ x in f, p x mean that {x | p x} ∈ f.MeasurableSpace : Type u_6 → Type u_6A measurable space is a space equipped with a σ-algebra.
StandardBorelSpace : (α : Type u_1) → [MeasurableSpace α] → PropA standard Borel space is a measurable space arising as the Borel sets of some Polish topology. This is useful in situations where a space has no natural topology or the natural topology in a space is non-Polish. To endow a standard Borel space `α` with a compatible Polish topology, use `letI := upgradeStandardBorel α`. One can then use `eq_borel_upgradeStandardBorel α` to rewrite the `MeasurableSpace α` instance to `borel α t`, where `t` is the new topology.
MeasureTheory.IsFiniteMeasure : {α : Type u_1} → {m0 : MeasurableSpace α} → MeasureTheory.Measure α → PropA measure `μ` is called finite if `μ univ < ∞`.
MeasureTheory.Measure : (α : Type u_5) → [MeasurableSpace α] → Type u_5A measure is defined to be an outer measure that is countably additive on measurable sets, with the additional assumption that the outer measure is the canonical extension of the restricted measure. The measure of a set `s`, denoted `μ s`, is an extended nonnegative real. The real-valued version is written `μ.real s`.
Real : TypeThe type `ℝ` of real numbers constructed as equivalence classes of Cauchy sequences of rational numbers.
LE.le : {α : Type u} → [self : LE α] → α → α → PropThe less-equal relation: `x ≤ y` Conventions for notations in identifiers: * The recommended spelling of `≤` in identifiers is `le`.
ProbabilityTheory.HasCondSubexponentialMGF : {Ω : Type u_1} →
(m : MeasurableSpace Ω) →
{mΩ : MeasurableSpace Ω} →
m ≤ mΩ →
[StandardBorelSpace Ω] →
(Ω → ℝ) →
ℝ →
ℝ →
(μ : autoParam (MeasureTheory.Measure Ω) ProbabilityTheory.HasCondSubexponentialMGF._auto_1) →
[MeasureTheory.IsFiniteMeasure μ] → PropA random variable `X` has a conditionally sub-exponential moment-generating function with parameters `(V, b)` with respect to a sigma-algebra `m` and a measure `μ` if for all `t` with `b * |t| ≤ 1`, `exp (t * X)` is `μ`-integrable and the moment-generating function of `X` conditioned on `m` is almost surely bounded by `exp (V * t ^ 2 / 2)`. The actual definition uses `Kernel.HasSubexponentialMGF`: `HasCondSubexponentialMGF` is defined as sub-exponential with respect to the conditional expectation kernel for `m` and the restriction of `μ` to the sigma-algebra `m`.Go to its page
Filter.Eventually : {α : Type u_1} → (α → Prop) → Filter α → Prop`f.Eventually p` or `∀ᶠ x in f, p x` mean that `{x | p x} ∈ f`. E.g., `∀ᶠ x in atTop, p x`
means that `p` holds true for sufficiently large `x`.MeasureTheory.Measure.trim : {α : Type u_1} → {m m0 : MeasurableSpace α} → MeasureTheory.Measure α → m ≤ m0 → MeasureTheory.Measure αRestriction of a measure to a sub-σ-algebra. It is common to see a measure `μ` on a measurable space structure `m0` as being also a measure on any `m ≤ m0`. Since measures in mathlib have to be trimmed to the measurable space, `μ` itself cannot be a measure on `m`, hence the definition of `μ.trim hm`. This notion is related to `OuterMeasure.trim`, see the lemma `toOuterMeasure_trim_eq_trim_toOuterMeasure`.
HMul.hMul : {α : Type u} → {β : Type v} → {γ : outParam (Type w)} → [self : HMul α β γ] → α → β → γ`a * b` computes the product of `a` and `b`. The meaning of this notation is type-dependent. Conventions for notations in identifiers: * The recommended spelling of `*` in identifiers is `mul`.
abs : {α : Type u_1} → [Lattice α] → [AddGroup α] → α → α`abs a`, denoted `|a|`, is the absolute value of `a`
ProbabilityTheory.cgf : {Ω : Type u_1} → {m : MeasurableSpace Ω} → (Ω → ℝ) → MeasureTheory.Measure Ω → ℝ → ℝCumulant-generating function of a real random variable `X`: `fun t => log μ[exp(t*X)]`.
ProbabilityTheory.condExpKernel : {Ω : Type u_3} →
[mΩ : MeasurableSpace Ω] →
[StandardBorelSpace Ω] →
(μ : MeasureTheory.Measure Ω) →
[MeasureTheory.IsFiniteMeasure μ] → (m : MeasurableSpace Ω) → ProbabilityTheory.Kernel Ω ΩKernel associated with the conditional expectation with respect to a σ-algebra. It satisfies `μ[f | m] =ᵐ[μ] fun ω => ∫ y, f y ∂(condExpKernel μ m ω)`. It is defined as the conditional distribution of the identity given the identity, where the second identity is understood as a map from `Ω` with the σ-algebra `mΩ` to `Ω` with σ-algebra `m ⊓ mΩ`. We use `m ⊓ mΩ` instead of `m` to ensure that it is a sub-σ-algebra of `mΩ`. We then use `Kernel.comap` to get a kernel from `m` to `mΩ` instead of from `m ⊓ mΩ` to `mΩ`.
HPow.hPow : {α : Type u} → {β : Type v} → {γ : outParam (Type w)} → [self : HPow α β γ] → α → β → γ`a ^ b` computes `a` to the power of `b`. The meaning of this notation is type-dependent. Conventions for notations in identifiers: * The recommended spelling of `^` in identifiers is `pow`.
HDiv.hDiv : {α : Type u} → {β : Type v} → {γ : outParam (Type w)} → [self : HDiv α β γ] → α → β → γ`a / b` computes the result of dividing `a` by `b`. The meaning of this notation is type-dependent. * For most types like `Nat`, `Int`, `Rat`, `Real`, `a / 0` is defined to be `0`. * For `Nat`, `a / b` rounds downwards. * For `Int`, `a / b` rounds downwards if `b` is positive or upwards if `b` is negative. It is implemented as `Int.ediv`, the unique function satisfying `a % b + b * (a / b) = a` and `0 ≤ a % b < natAbs b` for `b ≠ 0`. Other rounding conventions are available using the functions `Int.fdiv` (floor rounding) and `Int.tdiv` (truncation rounding). * For `Float`, `a / 0` follows the IEEE 754 semantics for division, usually resulting in `inf` or `nan`. Conventions for notations in identifiers: * The recommended spelling of `/` in identifiers is `div`.
Code
lemma cgf_le (h : HasCondSubexponentialMGF m hm X V b μ) (hV : 0 ≤ V) :
∀ᵐ ω' ∂(μ.trim hm), ∀ t, b * |t| ≤ 1 → cgf X (condExpKernel μ m ω') t ≤ V * t ^ 2 / 2Proof
Kernel.HasSubexponentialMGF.cgf_le h hV
Meaning last changed in v4.34.0-rc2-76-g565f652 (2026-09-10).
Self-contained, with its dependencies inlined and proofs replaced by sorry: download the raw file · open it in the Lean web editor.
Dependency graph
Audit surface: 2 project declarations, 68 external constants
✓ Proved: no sorry anywhere in its closure
This is the tool's own reading of one build's recorded axioms, and it is not robust against an author who wants it to pass. Checking meant to be relied on should go through Comparator, which replays the proof through the kernel from an export against an explicit list of permitted axioms.