BanditRLlib
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Lean module · Probability layer

BanditRLProof.ConcentrationMartingaleMaximal

This module supplies the martingale analytic dependency for source Theorem 9.2. Identifying an independent reward partial sum as this martingale is separate.

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Declarations
4
Placeholders
0

Imports

BanditRLProof.MartingaleDifference

Imported by

BanditRLProof, BanditRLProof.Algorithms.MOSSPeeling, BanditRLProof.ConcentrationIndexOccupancy

Declarations

Open an item to read its exact compact statement and source link. Detailed teaching notes are linked when registered.

theorem BanditRLProof.Concentration.submartingale_exp_of_martingale Compiled

Conditional Jensen turns an exponentially integrable real martingale into a nonnegative exponential submartingale.

Used in these reading views: Bandit Book · Reinforcement Learning Book · Online Learning Book

2. Probability, kernels, filtrations, and concentration

Canonical node identitydeclaration:BanditRLProof.Concentration.submartingale_exp_of_martingale

Reading membership is not a proof dependency. Exact assumptions remain in the Lean statement.

theorem submartingale_exp_of_martingale (hS : Martingale S F μ) (hint : ∀ i, Integrable (fun ω => exp (S i ω)) μ) : Submartingale (fun i ω => exp (S i ω)) F μ
theorem BanditRLProof.Concentration.measure_exists_le_martingale_ge_le_exp Compiled

Finite-time maximal Chernoff bound with no union-bound cardinality factor. The terminal MGF supplies the variance budget; all-time exponential integrability is explicit for the Jensen producer.

Used in these reading views: Bandit Book · Reinforcement Learning Book · Online Learning Book

2. Probability, kernels, filtrations, and concentration

Canonical node identitydeclaration:BanditRLProof.Concentration.measure_exists_le_martingale_ge_le_exp

Reading membership is not a proof dependency. Exact assumptions remain in the Lean statement.

theorem measure_exists_le_martingale_ge_le_exp (hS : Martingale S F μ) (hint : ∀ i t, Integrable (fun ω => exp (t * S i ω)) μ) (n : ℕ) (c : ℝ≥0) (hmgf : HasSubgaussianMGF (S n) c μ) (ε t : ℝ) (ht : 0 < t) : μ {ω | ∃ i, i ≤ n ∧ ε ≤ S i ω} ≤ ENNReal.ofReal (exp (-t * ε + (c : ℝ) * t ^ 2 / 2))
theorem BanditRLProof.Concentration.measure_exists_le_martingale_ge_le_subgaussian Compiled

Optimized finite maximal subgaussian bound.

Used in these reading views: Bandit Book · Reinforcement Learning Book · Online Learning Book

2. Probability, kernels, filtrations, and concentration

Canonical node identitydeclaration:BanditRLProof.Concentration.measure_exists_le_martingale_ge_le_subgaussian

Reading membership is not a proof dependency. Exact assumptions remain in the Lean statement.

theorem measure_exists_le_martingale_ge_le_subgaussian (hS : Martingale S F μ) (hint : ∀ i t, Integrable (fun ω => exp (t * S i ω)) μ) (n : ℕ) (c : ℝ≥0) (hc : 0 < (c : ℝ)) (hmgf : HasSubgaussianMGF (S n) c μ) (ε : ℝ) (hε : 0 < ε) : μ {ω | ∃ i, i ≤ n ∧ ε ≤ S i ω} ≤ ENNReal.ofReal (exp (-(ε ^ 2) / (2 * (c : ℝ))))
theorem BanditRLProof.Concentration.measure_exists_le_independent_partialSum_ge_le_subgaussian Compiled

Source Theorem 9.2 shape for independent centered subgaussian increments. The source variance is `c = σ²`; the partial sum uses X1 through Xn. Centering and coordinate measurability are explicit model contracts.

Used in these reading views: Bandit Book · Reinforcement Learning Book · Online Learning Book

2. Probability, kernels, filtrations, and concentration · Chapter 13: Lower Bounds: Basic Ideas

Canonical node identitydeclaration:BanditRLProof.Concentration.measure_exists_le_independent_partialSum_ge_le_subgaussian

Reading membership is not a proof dependency. Exact assumptions remain in the Lean statement.

theorem measure_exists_le_independent_partialSum_ge_le_subgaussian (X : ℕ → Ω → ℝ) (hXm : ∀ i, StronglyMeasurable (X i)) (hind : iIndepFun X μ) (hmean : ∀ i, ∫ ω, X i ω ∂μ = 0) (c : ℝ≥0) (hc : 0 < (c : ℝ)) (hsubG : ∀ i, HasSubgaussianMGF (X i) c μ) (n : ℕ) (hn : 0 < n) (ε : ℝ) (hε : 0 < ε) : μ {ω | ∃ i, i ≤ n ∧ ε ≤ ∑ j ∈ range i, X (j + 1) ω} ≤ ENNReal.ofReal (exp (-(ε ^ 2) / (2 * (n : ℝ) * (c : ℝ))))