openAI-math-dataset / lean /OAI /Probability /SKBarriers /Coverage /CoverageProbability.lean
roseking's picture
Add lean source and binary files (part 127)
135052a verified
Raw History Blame Contribute Delete
2.04 kB
import Mathlib
import OAI.Probability.SKBarriers.SpinGlass.LogPartition
namespace OAI
section
section
noncomputable section
open scoped BigOperators
open MeasureTheory ProbabilityTheory Filter Set
namespace SK.Analytic
theorem rotation_logPartition_tail {n : ℕ} (hn : 0 < n) (β θ u : ℝ) (hu : 0 ≤ u) :
(disorderPairLaw n).real {p | u ≤ logPartition β (rotateDisorder θ p).1-
logPartition β p.1} ≤ Real.exp (-u^2/(θ^2*β^2*(n:ℝ))) := by
let v : NNReal := ⟨θ^2*β^2*(n:ℝ)/2,by positivity⟩
have HS : HasSubgaussianMGF (fun p : Disorder n × Disorder n =>
logPartition β (rotateDisorder θ p).1-logPartition β p.1) v (disorderPairLaw n) := by
refine ⟨rotation_exp_integrable hn β θ,fun t => ?_⟩
apply (rotation_exp_integral_le hn β θ t).trans
apply Real.exp_le_exp.mpr
change t^2*θ^2*β^2*((n:ℝ)-1)/4 ≤ (θ^2*β^2*(n:ℝ)/2)*t^2/2
have H : 0 ≤ t^2*θ^2*β^2 := by positivity
nlinarith
convert HS.measure_ge_le hu using 1
congr 2
change θ^2*β^2*(n:ℝ) = 2*(θ^2*β^2*(n:ℝ)/2)
ring
theorem product_event_lower_of_sections {X Y : Type*} [MeasurableSpace X] [MeasurableSpace Y]
(μ : Measure X) (ν : Measure Y) [IsFiniteMeasure μ] [IsFiniteMeasure ν]
{A : Set X} {E : Set (X × Y)} (hA : MeasurableSet A) (hE : MeasurableSet E)
{c : ℝ} (hc : 0 ≤ c) (hsec : ∀ x ∈ A, c ≤ ν.real {y | (x,y) ∈ E}) :
c*μ.real A ≤ (μ.prod ν).real E := by
have H : ENNReal.ofReal c*μ A ≤ (μ.prod ν) E := by
rw [Measure.prod_apply hE,← lintegral_indicator_const hA (ENNReal.ofReal c)]
apply lintegral_mono
intro x
by_cases hx : x ∈ A
· rw [Set.indicator_of_mem hx]
exact (ENNReal.ofReal_le_iff_le_toReal (measure_ne_top ν _)).mpr (hsec x hx)
· rw [Set.indicator_of_notMem hx]
exact bot_le
have HT := ENNReal.toReal_mono (measure_ne_top (μ.prod ν) E) H
simpa only [ENNReal.toReal_mul,ENNReal.toReal_ofReal hc,measureReal_def] using HT
end SK.Analytic
end
end
end
end OAI