Learning.IT.filtration_eq_comap
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filtration_eq_comap๐
Learning.IT.filtration_eq_comapNo docstring.
Learning.IT.filtration_eq_comap.{u_1, u_2} {๐ : Type u_1} {๐จ : Type u_2} {m๐ : MeasurableSpace ๐} {m๐จ : MeasurableSpace ๐จ} (n : โ) : โ(IT.filtration ๐ ๐จ) n = MeasurableSpace.comap (hist n) inferInstanceLearning.IT.filtration_eq_comap.{u_1, u_2} {๐ : Type u_1} {๐จ : Type u_2} {m๐ : MeasurableSpace ๐} {m๐จ : MeasurableSpace ๐จ} (n : โ) : โ(IT.filtration ๐ ๐จ) n = MeasurableSpace.comap (hist n) inferInstance
Code
lemma filtration_eq_comap (n : โ) :
IT.filtration ๐ ๐จ n = MeasurableSpace.comap (hist n) inferInstanceType uses (2)
Body uses (1)
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Proof
by simp [IT.filtration, Filtration.piLE_eq_comap_frestrictLe, โ hist_eq_frestrictLe]
Dependency graph
Type dependencies (2)
filtration๐
Learning.IT.filtrationFiltration of the algorithm Seq.
Learning.IT.filtration.{u_4, u_5} (๐ : Type u_4) (๐จ : Type u_5) [MeasurableSpace ๐] [MeasurableSpace ๐จ] : MeasureTheory.Filtration โ inferInstanceLearning.IT.filtration.{u_4, u_5} (๐ : Type u_4) (๐จ : Type u_5) [MeasurableSpace ๐] [MeasurableSpace ๐จ] : MeasureTheory.Filtration โ inferInstance
Code
protected def filtration (๐ ๐จ : Type*) [MeasurableSpace ๐] [MeasurableSpace ๐จ] :
Filtration โ (inferInstance : MeasurableSpace (โ โ ๐ ร ๐จ)) :=
MeasureTheory.Filtration.piLE (X := fun _ โฆ ๐ ร ๐จ)Used by (13)
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hist๐
Learning.IT.hist
hist n is the history up to time n. This is a random variable on the measurable space
โ โ ๐ ร ๐จ.
Learning.IT.hist.{u_1, u_2} {๐ : Type u_1} {๐จ : Type u_2} (n : โ) (h : โ โ ๐ ร ๐จ) : โฅ(Finset.Iic n) โ ๐ ร ๐จLearning.IT.hist.{u_1, u_2} {๐ : Type u_1} {๐จ : Type u_2} (n : โ) (h : โ โ ๐ ร ๐จ) : โฅ(Finset.Iic n) โ ๐ ร ๐จ
Code
def hist (n : โ) (h : โ โ ๐ ร ๐จ) : Iic n โ ๐ ร ๐จ := fun i โฆ h i
Used by (23)
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