Laws of stepsUntil and rewardByCount

theorem Bandits.measurable_pullCount {α : Type u_1} {mα : MeasurableSpace α} [DecidableEq α] [MeasurableSingletonClass α] (a : α) (t : ℕ) : Measurable fun (h : ℕ → α × ℝ) => pullCount a t h

theorem Bandits.measurable_sumRewards {α : Type u_1} {mα : MeasurableSpace α} [DecidableEq α] [MeasurableSingletonClass α] (a : α) (t : ℕ) : Measurable (sumRewards a t)

theorem Bandits.measurable_stepsUntil {α : Type u_1} {mα : MeasurableSpace α} [DecidableEq α] [MeasurableSingletonClass α] (a : α) (m : ℕ) : Measurable fun (h : ℕ → α × ℝ) => stepsUntil a m h

theorem Bandits.measurable_stepsUntil' {α : Type u_1} {mα : MeasurableSpace α} [DecidableEq α] [MeasurableSingletonClass α] (a : α) (m : ℕ) : Measurable fun (ω : (ℕ → α × ℝ) × (ℕ → α → ℝ)) => stepsUntil a m ω.1

theorem Bandits.measurable_rewardByCount {α : Type u_1} {mα : MeasurableSpace α} [DecidableEq α] [MeasurableSingletonClass α] (a : α) (m : ℕ) : Measurable fun (ω : (ℕ → α × ℝ) × (ℕ → α → ℝ)) => rewardByCount a m ω.1 ω.2

theorem Bandits.hasLaw_Z {α : Type u_1} {mα : MeasurableSpace α} {alg : Learning.Algorithm α ℝ} {ν : ProbabilityTheory.Kernel α ℝ} [ProbabilityTheory.IsMarkovKernel ν] (a : α) (m : ℕ) : ProbabilityTheory.HasLaw (fun (ω : (ℕ → α × ℝ) × (ℕ → α → ℝ)) => ω.2 m a) (ν a) (Bandit.measure alg ν)

def Bandits.«term𝓛[_|_;_]» : Lean.ParserDescr

Law of Y conditioned on the event s.

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def Bandits.«term𝓛[_|_In_;_]» : Lean.ParserDescr

Law of Y conditioned on the event that X is in s.

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def Bandits.«term𝓛[_|_←_;_]» : Lean.ParserDescr

Law of Y conditioned on the event that X equals x.

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def Bandits.«term𝓛[_|_;_]_1» : Lean.ParserDescr

Law of Y conditioned on X.

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• One or more equations did not get rendered due to their size.

theorem Bandits.condDistrib_reward'' {α : Type u_1} {mα : MeasurableSpace α} {alg : Learning.Algorithm α ℝ} {ν : ProbabilityTheory.Kernel α ℝ} [ProbabilityTheory.IsMarkovKernel ν] [StandardBorelSpace α] [Nonempty α] (n : ℕ) : ⇑𝓛[fun (ω : (ℕ → α × ℝ) × (ℕ → α → ℝ)) => reward n ω.1 | fun (ω : (ℕ → α × ℝ) × (ℕ → α → ℝ)) => arm n ω.1; Bandit.measure alg ν] =ᵐ[MeasureTheory.Measure.map (fun (ω : (ℕ → α × ℝ) × (ℕ → α → ℝ)) => arm n ω.1) (Bandit.measure alg ν)] ⇑ν

theorem Bandits.reward_cond_arm {α : Type u_1} {mα : MeasurableSpace α} [MeasurableSingletonClass α] {alg : Learning.Algorithm α ℝ} {ν : ProbabilityTheory.Kernel α ℝ} [ProbabilityTheory.IsMarkovKernel ν] [StandardBorelSpace α] [Nonempty α] [Countable α] (a : α) (n : ℕ) (hμa : (MeasureTheory.Measure.map (fun (ω : (ℕ → α × ℝ) × (ℕ → α → ℝ)) => arm n ω.1) (Bandit.measure alg ν)) {a} ≠ 0) : 𝓛[fun (ω : (ℕ → α × ℝ) × (ℕ → α → ℝ)) => reward n ω.1 | fun (ω : (ℕ → α × ℝ) × (ℕ → α → ℝ)) => arm n ω.1 in {a}; Bandit.measure alg ν] = ν a

theorem Bandits.condIndepFun_reward_stepsUntil_arm {α : Type u_1} {mα : MeasurableSpace α} [DecidableEq α] [MeasurableSingletonClass α] {alg : Learning.Algorithm α ℝ} {ν : ProbabilityTheory.Kernel α ℝ} [ProbabilityTheory.IsMarkovKernel ν] [StandardBorelSpace α] [Countable α] [Nonempty α] (a : α) (m n : ℕ) (hm : m ≠ 0) : ProbabilityTheory.CondIndepFun (MeasurableSpace.comap (fun (ω : (ℕ → α × ℝ) × (ℕ → α → ℝ)) => arm n ω.1) mα) ⋯ (fun (ω : (ℕ → α × ℝ) × (ℕ → α → ℝ)) => reward n ω.1) ({ω : (ℕ → α × ℝ) × (ℕ → α → ℝ) | stepsUntil a m ω.1 = ↑n}.indicator fun (x : (ℕ → α × ℝ) × (ℕ → α → ℝ)) => 1) (Bandit.measure alg ν)

theorem Bandits.reward_cond_stepsUntil {α : Type u_1} {mα : MeasurableSpace α} [DecidableEq α] [MeasurableSingletonClass α] {alg : Learning.Algorithm α ℝ} {ν : ProbabilityTheory.Kernel α ℝ} [ProbabilityTheory.IsMarkovKernel ν] [StandardBorelSpace α] [Countable α] [Nonempty α] (a : α) (m n : ℕ) (hm : m ≠ 0) (hμn : (Bandit.measure alg ν) ((fun (ω : (ℕ → α × ℝ) × (ℕ → α → ℝ)) => stepsUntil a m ω.1) ⁻¹' {↑n}) ≠ 0) : 𝓛[fun (ω : (ℕ → α × ℝ) × (ℕ → α → ℝ)) => reward n ω.1 | fun (ω : (ℕ → α × ℝ) × (ℕ → α → ℝ)) => stepsUntil a m ω.1 in {↑n}; Bandit.measure alg ν] = ν a

theorem Bandits.condDistrib_rewardByCount_stepsUntil {α : Type u_1} {mα : MeasurableSpace α} [DecidableEq α] [MeasurableSingletonClass α] {alg : Learning.Algorithm α ℝ} {ν : ProbabilityTheory.Kernel α ℝ} [ProbabilityTheory.IsMarkovKernel ν] [Countable α] [StandardBorelSpace α] [Nonempty α] (a : α) (m : ℕ) (hm : m ≠ 0) : ⇑𝓛[fun (ω : (ℕ → α × ℝ) × (ℕ → α → ℝ)) => rewardByCount a m ω.1 ω.2 | fun (ω : (ℕ → α × ℝ) × (ℕ → α → ℝ)) => stepsUntil a m ω.1; Bandit.measure alg ν] =ᵐ[MeasureTheory.Measure.map (fun (ω : (ℕ → α × ℝ) × (ℕ → α → ℝ)) => stepsUntil a m ω.1) (Bandit.measure alg ν)] ⇑(ProbabilityTheory.Kernel.const ℕ∞ (ν a))

theorem Bandits.hasLaw_rewardByCount {α : Type u_1} {mα : MeasurableSpace α} [DecidableEq α] [MeasurableSingletonClass α] {alg : Learning.Algorithm α ℝ} {ν : ProbabilityTheory.Kernel α ℝ} [ProbabilityTheory.IsMarkovKernel ν] [Countable α] [StandardBorelSpace α] [Nonempty α] (a : α) (m : ℕ) (hm : m ≠ 0) : ProbabilityTheory.HasLaw (fun (ω : (ℕ → α × ℝ) × (ℕ → α → ℝ)) => rewardByCount a m ω.1 ω.2) (ν a) (Bandit.measure alg ν)

The reward received at the m-th pull of arm a has law ν a.

theorem Bandits.identDistrib_rewardByCount {α : Type u_1} {mα : MeasurableSpace α} [DecidableEq α] [MeasurableSingletonClass α] {alg : Learning.Algorithm α ℝ} {ν : ProbabilityTheory.Kernel α ℝ} [ProbabilityTheory.IsMarkovKernel ν] [Countable α] [StandardBorelSpace α] [Nonempty α] (a : α) (n m : ℕ) (hn : n ≠ 0) (hm : m ≠ 0) : ProbabilityTheory.IdentDistrib (fun (ω : (ℕ → α × ℝ) × (ℕ → α → ℝ)) => rewardByCount a n ω.1 ω.2) (fun (ω : (ℕ → α × ℝ) × (ℕ → α → ℝ)) => rewardByCount a m ω.1 ω.2) (Bandit.measure alg ν) (Bandit.measure alg ν)

theorem Bandits.identDistrib_rewardByCount_id {α : Type u_1} {mα : MeasurableSpace α} [DecidableEq α] [MeasurableSingletonClass α] {alg : Learning.Algorithm α ℝ} {ν : ProbabilityTheory.Kernel α ℝ} [ProbabilityTheory.IsMarkovKernel ν] [Countable α] [StandardBorelSpace α] [Nonempty α] (a : α) (n : ℕ) (hn : n ≠ 0) : ProbabilityTheory.IdentDistrib (fun (ω : (ℕ → α × ℝ) × (ℕ → α → ℝ)) => rewardByCount a n ω.1 ω.2) id (Bandit.measure alg ν) (ν a)

theorem Bandits.identDistrib_rewardByCount_eval {α : Type u_1} {mα : MeasurableSpace α} [DecidableEq α] [MeasurableSingletonClass α] {alg : Learning.Algorithm α ℝ} {ν : ProbabilityTheory.Kernel α ℝ} [ProbabilityTheory.IsMarkovKernel ν] [Countable α] [StandardBorelSpace α] [Nonempty α] (a : α) (n m : ℕ) (hn : n ≠ 0) : ProbabilityTheory.IdentDistrib (fun (ω : (ℕ → α × ℝ) × (ℕ → α → ℝ)) => rewardByCount a n ω.1 ω.2) (fun (ω : ℕ → α → ℝ) => ω m a) (Bandit.measure alg ν) (Bandit.streamMeasure ν)

theorem Bandits.iIndepFun_rewardByCount {α : Type u_1} {mα : MeasurableSpace α} [DecidableEq α] [MeasurableSingletonClass α] (alg : Learning.Algorithm α ℝ) (ν : ProbabilityTheory.Kernel α ℝ) [ProbabilityTheory.IsMarkovKernel ν] : ProbabilityTheory.iIndepFun (fun (p : α × ℕ) (ω : (ℕ → α × ℝ) × (ℕ → α → ℝ)) => rewardByCount p.1 p.2 ω.1 ω.2) (Bandit.measure alg ν)