OAI/GroupTheory/PolycyclicRecognition/Analysis/BackwardSwitchProbability.lean
7 declarations, 0 contain sorry, 0 declared as axioms
Probability of retaining every intermediate sheet
- def
OAI.PolycyclicRecognition.survivingSwitchProbabilityline 18Source documentation
Iterate the actual independent pairs of increment samples, killing a path at its first exit from the prescribed next set.
def survivingSwitchProbability (σ : ℕ → Measure G) (S : ℕ → Set G) : ℕ → ℕ → G → ℝ≥0∞ | _, 0, _ => 1 | n, k + 1, x => ∫⁻ s₁, ∫⁻ s₂, (S (n + 1)).indicator (survivingSwitchProbability σ S (n + 1) k) (x * s₁ * s₂⁻¹) ∂σ n ∂σ n /-- The product of the successive transition thresholds, in the same recursive order as the independent sampling. -/ - def
OAI.PolycyclicRecognition.switchThresholdProductline 27Source documentation
The product of the successive transition thresholds, in the same recursive order as the independent sampling.
def switchThresholdProduct (τ : ℕ → ℝ≥0) : ℕ → ℕ → ℝ≥0∞ | _, 0 => 1 | n, k + 1 => τ n * switchThresholdProduct τ (n + 1) k
- theorem
OAI.PolycyclicRecognition.switchThresholdProduct_ne_topline 31 - theorem
OAI.PolycyclicRecognition.switchThresholdProduct_posline 37 - theorem
OAI.PolycyclicRecognition.survivingSwitchProbability_le_oneline 47 - theorem
OAI.PolycyclicRecognition.measurable_survivingSwitchProbabilityline 65 - theorem
OAI.PolycyclicRecognition.switchThresholdProduct_le_survivalline 82Source documentation
Independent sampling multiplies the conditional return bounds. The sets may differ at each stage, which is essential for backward recurrence.
theorem switchThresholdProduct_le_survival (σ : ℕ → Measure G) (S : ℕ → Set G) (τ : ℕ → ℝ≥0) (n k : ℕ) (hreturn : ∀ j, j < n + k → ∀ x ∈ S j, (τ j : ℝ≥0∞) ≤ twoSampleReturn (σ j) (S (j + 1)) x) {x : G} (hx : x ∈ S n) : switchThresholdProduct τ n k ≤ survivingSwitchProbability σ S n k x := by …Mentions: OAI.PolycyclicRecognition.twoSampleReturn, OAI.PolycyclicRecognition.switchThresholdProduct, OAI.PolycyclicRecognition.survivingSwitchProbability