ProbabilityTheory.Kernel.HasSubexponentialMGF.fun_neg
From the authors
Eta-expanded form of ProbabilityTheory.Kernel.HasSubexponentialMGF.neg
-
Ω : Type u_1mΩ : MeasurableSpace ΩA measurable space is a space equipped with a σ-algebra. -
Ω' : Type u_2mΩ' : MeasurableSpace Ω'
-
ν : MeasureTheory.Measure Ω'A 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. -
κ : Kernel Ω' ΩA kernel from a measurable spaceαto another measurable spaceβis a measurable functionκ : α → Measure β. -
X : Ω → ℝ -
V : ℝ -
b : ℝ
HasSubexponentialMGF (fun i => -X i) V b κ νMeasurableSpace : Type u_6 → Type u_6A measurable space is a space equipped with a σ-algebra.
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`.
ProbabilityTheory.Kernel : (α : Type u_1) → (β : Type u_2) → [MeasurableSpace α] → [MeasurableSpace β] → Type (max u_1 u_2)A kernel from a measurable space `α` to another measurable space `β` is a measurable function `κ : α → Measure β`. The measurable space structure on `MeasureTheory.Measure β` is given by `MeasureTheory.Measure.instMeasurableSpace`. A map `κ : α → MeasureTheory.Measure β` is measurable iff `∀ s : Set β, MeasurableSet s → Measurable (fun a ↦ κ a s)`.
Real : TypeThe type `ℝ` of real numbers constructed as equivalence classes of Cauchy sequences of rational numbers.
ProbabilityTheory.Kernel.HasSubexponentialMGF : {Ω : Type u_1} →
{Ω' : Type u_2} →
{mΩ : MeasurableSpace Ω} →
{mΩ' : MeasurableSpace Ω'} →
(Ω → ℝ) →
ℝ →
ℝ →
ProbabilityTheory.Kernel Ω' Ω →
autoParam (MeasureTheory.Measure Ω') ProbabilityTheory.Kernel.HasSubexponentialMGF._auto_1 → PropA random variable `X` has a sub-exponential moment-generating function with parameters `(V, b)` with respect to a kernel `κ` and a measure `ν` if for every `t` with `b * |t| ≤ 1`, `exp (t * X)` is integrable with respect to `κ ∘ₘ ν` and, for `ν`-almost all `ω'`, the moment-generating function of `X` with respect to `κ ω'` is bounded by `exp (V * t ^ 2 / 2)`. For `b = 0` this is `Kernel.HasSubgaussianMGF X V κ ν`.Go to its page
Neg.neg : {α : Type u} → [self : Neg α] → α → α`-a` computes the negative or opposite of `a`. The meaning of this notation is type-dependent. Conventions for notations in identifiers: * The recommended spelling of `-` in identifiers is `neg` (when used as a unary operator).
Code
lemma fun_neg : ∀ {Ω : Type u_1} {Ω' : Type u_2} {mΩ : MeasurableSpace Ω} {mΩ' : MeasurableSpace Ω'} {ν : MeasureTheory.Measure Ω'}
{κ : ProbabilityTheory.Kernel Ω' Ω} {X : Ω → ℝ} {V b : ℝ},
ProbabilityTheory.Kernel.HasSubexponentialMGF X V b κ ν →
ProbabilityTheory.Kernel.HasSubexponentialMGF (fun i => -X i) V b κ νProof
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: 1 project declarations, 62 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.