Independence with respect to a kernel and a measure #
A family of sets of sets π : ι → Set (Set Ω)
is independent with respect to a kernel
κ : Kernel α Ω
and a measure μ
on α
if for any finite set of indices s = {i_1, ..., i_n}
,
for any sets f i_1 ∈ π i_1, ..., f i_n ∈ π i_n
, then for μ
-almost every a : α
,
κ a (⋂ i in s, f i) = ∏ i ∈ s, κ a (f i)
.
This notion of independence is a generalization of both independence and conditional independence.
For conditional independence, κ
is the conditional kernel ProbabilityTheory.condexpKernel
and
μ
is the ambiant measure. For (non-conditional) independence, κ = Kernel.const Unit μ
and the
measure is the Dirac measure on Unit
.
The main purpose of this file is to prove only once the properties that hold for both conditional and non-conditional independence.
Main definitions #
ProbabilityTheory.Kernel.iIndepSets
: independence of a family of sets of sets. Variant for two sets of sets:ProbabilityTheory.Kernel.IndepSets
.ProbabilityTheory.Kernel.iIndep
: independence of a family of σ-algebras. Variant for two σ-algebras:Indep
.ProbabilityTheory.Kernel.iIndepSet
: independence of a family of sets. Variant for two sets:ProbabilityTheory.Kernel.IndepSet
.ProbabilityTheory.Kernel.iIndepFun
: independence of a family of functions (random variables). Variant for two functions:ProbabilityTheory.Kernel.IndepFun
.
See the file Mathlib/Probability/Kernel/Basic.lean
for a more detailed discussion of these
definitions in the particular case of the usual independence notion.
Main statements #
ProbabilityTheory.Kernel.iIndepSets.iIndep
: if π-systems are independent as sets of sets, then the measurable space structures they generate are independent.ProbabilityTheory.Kernel.IndepSets.Indep
: variant with two π-systems.
A family of sets of sets π : ι → Set (Set Ω)
is independent with respect to a kernel κ
and
a measure μ
if for any finite set of indices s = {i_1, ..., i_n}
, for any sets
f i_1 ∈ π i_1, ..., f i_n ∈ π i_n
, then ∀ᵐ a ∂μ, κ a (⋂ i in s, f i) = ∏ i ∈ s, κ a (f i)
.
It will be used for families of pi_systems.
Equations
Instances For
Two sets of sets s₁, s₂
are independent with respect to a kernel κ
and a measure μ
if for
any sets t₁ ∈ s₁, t₂ ∈ s₂
, then ∀ᵐ a ∂μ, κ a (t₁ ∩ t₂) = κ a (t₁) * κ a (t₂)
Equations
Instances For
A family of measurable space structures (i.e. of σ-algebras) is independent with respect to a
kernel κ
and a measure μ
if the family of sets of measurable sets they define is independent.
Equations
- ProbabilityTheory.Kernel.iIndep m κ μ = ProbabilityTheory.Kernel.iIndepSets (fun (x : ι) => {s : Set Ω | MeasurableSet s}) κ μ
Instances For
Two measurable space structures (or σ-algebras) m₁, m₂
are independent with respect to a
kernel κ
and a measure μ
if for any sets t₁ ∈ m₁, t₂ ∈ m₂
,
∀ᵐ a ∂μ, κ a (t₁ ∩ t₂) = κ a (t₁) * κ a (t₂)
Equations
- ProbabilityTheory.Kernel.Indep m₁ m₂ κ μ = ProbabilityTheory.Kernel.IndepSets {s : Set Ω | MeasurableSet s} {s : Set Ω | MeasurableSet s} κ μ
Instances For
A family of sets is independent if the family of measurable space structures they generate is
independent. For a set s
, the generated measurable space has measurable sets ∅, s, sᶜ, univ
.
Equations
- ProbabilityTheory.Kernel.iIndepSet s κ μ = ProbabilityTheory.Kernel.iIndep (fun (i : ι) => MeasurableSpace.generateFrom {s i}) κ μ
Instances For
Two sets are independent if the two measurable space structures they generate are independent.
For a set s
, the generated measurable space structure has measurable sets ∅, s, sᶜ, univ
.
Equations
Instances For
A family of functions defined on the same space Ω
and taking values in possibly different
spaces, each with a measurable space structure, is independent if the family of measurable space
structures they generate on Ω
is independent. For a function g
with codomain having measurable
space structure m
, the generated measurable space structure is MeasurableSpace.comap g m
.
Equations
- ProbabilityTheory.Kernel.iIndepFun m f κ μ = ProbabilityTheory.Kernel.iIndep (fun (x : ι) => MeasurableSpace.comap (f x) (m x)) κ μ
Instances For
Two functions are independent if the two measurable space structures they generate are
independent. For a function f
with codomain having measurable space structure m
, the generated
measurable space structure is MeasurableSpace.comap f m
.
Equations
- ProbabilityTheory.Kernel.IndepFun f g κ μ = ProbabilityTheory.Kernel.Indep (MeasurableSpace.comap f mβ) (MeasurableSpace.comap g mγ) κ μ
Instances For
Alias of the forward direction of ProbabilityTheory.Kernel.iIndepSets_congr
.
Alias of the forward direction of ProbabilityTheory.Kernel.indepSets_congr
.
Alias of the forward direction of ProbabilityTheory.Kernel.iIndep_congr
.
Alias of the forward direction of ProbabilityTheory.Kernel.indep_congr
.
Alias of the forward direction of ProbabilityTheory.Kernel.iIndepSet_congr
.
Alias of the forward direction of ProbabilityTheory.Kernel.indepSet_congr
.
Alias of the forward direction of ProbabilityTheory.Kernel.iIndepFun_congr
.
Alias of the forward direction of ProbabilityTheory.Kernel.indepFun_congr
.
π-system lemma #
Independence of measurable spaces is equivalent to independence of generating π-systems.
Independence of measurable space structures implies independence of generating π-systems #
Independence of generating π-systems implies independence of measurable space structures #
The measurable space structures generated by independent pi-systems are independent.
The measurable space structures generated by independent pi-systems are independent.
Independence of measurable sets #
We prove the following equivalences on IndepSet
, for measurable sets s, t
.
Independence of random variables #
Alias of the forward direction of ProbabilityTheory.Kernel.indepFun_iff_measure_inter_preimage_eq_mul
.
Alias of the forward direction of ProbabilityTheory.Kernel.iIndepFun_iff_measure_inter_preimage_eq_mul
.
If f
is a family of mutually independent random variables (iIndepFun m f μ
) and S, T
are
two disjoint finite index sets, then the tuple formed by f i
for i ∈ S
is independent of the
tuple (f i)_i
for i ∈ T
.