# Measure spaces #

The definition of a measure and a measure space are in `measure_theory.measure_space_def`

, with
only a few basic properties. This file provides many more properties of these objects.
This separation allows the measurability tactic to import only the file `measure_space_def`

, and to
be available in `measure_space`

(through `measurable_space`

).

Given a measurable space `α`

, a measure on `α`

is a function that sends measurable sets to the
extended nonnegative reals that satisfies the following conditions:

`μ ∅ = 0`

;`μ`

is countably additive. This means that the measure of a countable union of pairwise disjoint sets is equal to the measure of the individual sets.

Every measure can be canonically extended to an outer measure, so that it assigns values to all subsets, not just the measurable subsets. On the other hand, a measure that is countably additive on measurable sets can be restricted to measurable sets to obtain a measure. In this file a measure is defined to be an outer measure that is countably additive on measurable sets, with the additional assumption that the outer measure is the canonical extension of the restricted measure.

Measures on `α`

form a complete lattice, and are closed under scalar multiplication with `ℝ≥0∞`

.

We introduce the following typeclasses for measures:

`is_probability_measure μ`

:`μ univ = 1`

;`is_finite_measure μ`

:`μ univ < ∞`

;`sigma_finite μ`

: there exists a countable collection of sets that cover`univ`

where`μ`

is finite;`is_locally_finite_measure μ`

:`∀ x, ∃ s ∈ 𝓝 x, μ s < ∞`

;`has_no_atoms μ`

:`∀ x, μ {x} = 0`

; possibly should be redefined as`∀ s, 0 < μ s → ∃ t ⊆ s, 0 < μ t ∧ μ t < μ s`

.

Given a measure, the null sets are the sets where `μ s = 0`

, where `μ`

denotes the corresponding
outer measure (so `s`

might not be measurable). We can then define the completion of `μ`

as the
measure on the least `σ`

-algebra that also contains all null sets, by defining the measure to be `0`

on the null sets.

## Main statements #

`completion`

is the completion of a measure to all null measurable sets.`measure.of_measurable`

and`outer_measure.to_measure`

are two important ways to define a measure.

## Implementation notes #

Given `μ : measure α`

, `μ s`

is the value of the *outer measure* applied to `s`

.
This conveniently allows us to apply the measure to sets without proving that they are measurable.
We get countable subadditivity for all sets, but only countable additivity for measurable sets.

You often don't want to define a measure via its constructor. Two ways that are sometimes more convenient:

`measure.of_measurable`

is a way to define a measure by only giving its value on measurable sets and proving the properties (1) and (2) mentioned above.`outer_measure.to_measure`

is a way of obtaining a measure from an outer measure by showing that all measurable sets in the measurable space are Carathéodory measurable.

To prove that two measures are equal, there are multiple options:

`ext`

: two measures are equal if they are equal on all measurable sets.`ext_of_generate_from_of_Union`

: two measures are equal if they are equal on a π-system generating the measurable sets, if the π-system contains a spanning increasing sequence of sets where the measures take finite value (in particular the measures are σ-finite). This is a special case of the more general`ext_of_generate_from_of_cover`

`ext_of_generate_finite`

: two finite measures are equal if they are equal on a π-system generating the measurable sets. This is a special case of`ext_of_generate_from_of_Union`

using`C ∪ {univ}`

, but is easier to work with.

A `measure_space`

is a class that is a measurable space with a canonical measure.
The measure is denoted `volume`

.

## References #

- https://en.wikipedia.org/wiki/Measure_(mathematics)
- https://en.wikipedia.org/wiki/Complete_measure
- https://en.wikipedia.org/wiki/Almost_everywhere

## Tags #

measure, almost everywhere, measure space, completion, null set, null measurable set

If `s`

is a countable set, then the measure of its preimage can be found as the sum of measures
of the fibers `f ⁻¹' {y}`

.

If `s`

is a `finset`

, then the measure of its preimage can be found as the sum of measures
of the fibers `f ⁻¹' {y}`

.

Pigeonhole principle for measure spaces: if `∑' i, μ (s i) > μ univ`

, then
one of the intersections `s i ∩ s j`

is not empty.

Pigeonhole principle for measure spaces: if `s`

is a `finset`

and
`∑ i in s, μ (t i) > μ univ`

, then one of the intersections `t i ∩ t j`

is not empty.

Continuity from below: the measure of the union of a directed sequence of measurable sets is the supremum of the measures.

Continuity from above: the measure of the intersection of a decreasing sequence of measurable sets is the infimum of the measures.

Continuity from below: the measure of the union of an increasing sequence of measurable sets is the limit of the measures.

Continuity from above: the measure of the intersection of a decreasing sequence of measurable sets is the limit of the measures.

One direction of the **Borel-Cantelli lemma**: if (sᵢ) is a sequence of sets such
that `∑ μ sᵢ`

is finite, then the limit superior of the `sᵢ`

is a null set.

Obtain a measure by giving an outer measure where all sets in the σ-algebra are Carathéodory measurable.

## Equations

- m.to_measure h = measure_theory.measure.of_measurable (λ (s : set α) (_x : measurable_set s), ⇑m s) _ _

### The `ℝ≥0∞`

-module of measures #

## Equations

- measure_theory.measure.has_zero = {zero := {to_outer_measure := 0, m_Union := _, trimmed := _}}

## Equations

- measure_theory.measure.has_add = {add := λ (μ₁ μ₂ : measure_theory.measure α), {to_outer_measure := μ₁.to_outer_measure + μ₂.to_outer_measure, m_Union := _, trimmed := _}}

## Equations

- measure_theory.measure.has_scalar = {smul := λ (c : ℝ≥0∞) (μ : measure_theory.measure α), {to_outer_measure := c • μ.to_outer_measure, m_Union := _, trimmed := _}}

### The complete lattice of measures #

Measures are partially ordered.

The definition of less equal here is equivalent to the definition without the
measurable set condition, and this is shown by `measure.le_iff'`

. It is defined
this way since, to prove `μ ≤ ν`

, we may simply `intros s hs`

instead of rewriting followed
by `intros s hs`

.

## Equations

- measure_theory.measure.partial_order = {le := λ (m₁ m₂ : measure_theory.measure α), ∀ (s : set α), measurable_set s → ⇑m₁ s ≤ ⇑m₂ s, lt := preorder.lt._default (λ (m₁ m₂ : measure_theory.measure α), ∀ (s : set α), measurable_set s → ⇑m₁ s ≤ ⇑m₂ s), le_refl := _, le_trans := _, lt_iff_le_not_le := _, le_antisymm := _}

## Equations

- measure_theory.measure.complete_semilattice_Inf = {le := partial_order.le measure_theory.measure.partial_order, lt := partial_order.lt measure_theory.measure.partial_order, le_refl := _, le_trans := _, lt_iff_le_not_le := _, le_antisymm := _, Inf := Inf measure_theory.measure.has_Inf, Inf_le := _, le_Inf := _}

## Equations

- measure_theory.measure.complete_lattice = {sup := complete_lattice.sup (complete_lattice_of_complete_semilattice_Inf (measure_theory.measure α)), le := complete_lattice.le (complete_lattice_of_complete_semilattice_Inf (measure_theory.measure α)), lt := complete_lattice.lt (complete_lattice_of_complete_semilattice_Inf (measure_theory.measure α)), le_refl := _, le_trans := _, lt_iff_le_not_le := _, le_antisymm := _, le_sup_left := _, le_sup_right := _, sup_le := _, inf := complete_lattice.inf (complete_lattice_of_complete_semilattice_Inf (measure_theory.measure α)), inf_le_left := _, inf_le_right := _, le_inf := _, top := complete_lattice.top (complete_lattice_of_complete_semilattice_Inf (measure_theory.measure α)), le_top := _, bot := 0, bot_le := _, Sup := complete_lattice.Sup (complete_lattice_of_complete_semilattice_Inf (measure_theory.measure α)), le_Sup := _, Sup_le := _, Inf := complete_lattice.Inf (complete_lattice_of_complete_semilattice_Inf (measure_theory.measure α)), Inf_le := _, le_Inf := _}

### Pushforward and pullback #

Lift a linear map between `outer_measure`

spaces such that for each measure `μ`

every measurable
set is caratheodory-measurable w.r.t. `f μ`

to a linear map between `measure`

spaces.

## Equations

- measure_theory.measure.lift_linear f hf = {to_fun := λ (μ : measure_theory.measure α), (⇑f μ.to_outer_measure).to_measure _, map_add' := _, map_smul' := _}

The pushforward of a measure. It is defined to be `0`

if `f`

is not a measurable function.

## Equations

- measure_theory.measure.map f = dite (measurable f) (λ (hf : measurable f), measure_theory.measure.lift_linear (measure_theory.outer_measure.map f) _) (λ (hf : ¬measurable f), 0)

We can evaluate the pushforward on measurable sets. For non-measurable sets, see
`measure_theory.measure.le_map_apply`

and `measurable_equiv.map_apply`

.

Even if `s`

is not measurable, we can bound `map f μ s`

from below.
See also `measurable_equiv.map_apply`

.

Even if `s`

is not measurable, `map f μ s = 0`

implies that `μ (f ⁻¹' s) = 0`

.

Pullback of a `measure`

. If `f`

sends each `measurable`

set to a `measurable`

set, then for each
measurable set `s`

we have `comap f μ s = μ (f '' s)`

.

## Equations

- measure_theory.measure.comap f = dite (function.injective f ∧ ∀ (s : set α), measurable_set s → measurable_set (f '' s)) (λ (hf : function.injective f ∧ ∀ (s : set α), measurable_set s → measurable_set (f '' s)), measure_theory.measure.lift_linear (measure_theory.outer_measure.comap f) _) (λ (hf : ¬(function.injective f ∧ ∀ (s : set α), measurable_set s → measurable_set (f '' s))), 0)

### Restricting a measure #

Restrict a measure `μ`

to a set `s`

as an `ℝ≥0∞`

-linear map.

Restrict a measure `μ`

to a set `s`

.

## Equations

- μ.restrict s = ⇑(measure_theory.measure.restrictₗ s) μ

If `t`

is a measurable set, then the measure of `t`

with respect to the restriction of
the measure to `s`

equals the outer measure of `t ∩ s`

. An alternate version requiring that `s`

be measurable instead of `t`

exists as `measure.restrict_apply'`

.

Restriction of a measure to a subset is monotone both in set and in measure.

Restriction of a measure to a subset is monotone both in set and in measure.

If two measures agree on all measurable subsets of `s`

and `t`

, then they agree on all
measurable subsets of `s ∪ t`

.

This lemma shows that `restrict`

and `to_outer_measure`

commute. Note that the LHS has a
restrict on measures and the RHS has a restrict on outer measures.

This lemma shows that `Inf`

and `restrict`

commute for measures.

If `s`

is a measurable set, then the outer measure of `t`

with respect to the restriction of
the measure to `s`

equals the outer measure of `t ∩ s`

. This is an alternate version of
`measure.restrict_apply`

, requiring that `s`

is measurable instead of `t`

.

### Extensionality results #

Two measures are equal if they have equal restrictions on a spanning collection of sets
(formulated using `Union`

).

**Alias** of `ext_iff_of_Union_eq_univ`

.

Two measures are equal if they have equal restrictions on a spanning collection of sets
(formulated using `bUnion`

).

**Alias** of `ext_iff_of_bUnion_eq_univ`

.

Two measures are equal if they have equal restrictions on a spanning collection of sets
(formulated using `sUnion`

).

**Alias** of `ext_iff_of_sUnion_eq_univ`

.

Two measures are equal if they are equal on the π-system generating the σ-algebra,
and they are both finite on a increasing spanning sequence of sets in the π-system.
This lemma is formulated using `sUnion`

.

Two measures are equal if they are equal on the π-system generating the σ-algebra,
and they are both finite on a increasing spanning sequence of sets in the π-system.
This lemma is formulated using `Union`

.
`finite_spanning_sets_in.ext`

is a reformulation of this lemma.

The dirac measure.

## Equations

Sum of an indexed family of measures.

## Equations

- measure_theory.measure.sum f = (measure_theory.outer_measure.sum (λ (i : ι), (f i).to_outer_measure)).to_measure _

If `f`

is a map with encodable codomain, then `map f μ`

is the sum of Dirac measures

A measure on an encodable type is a sum of dirac measures.

Counting measure on any measurable space.

`count`

measure evaluates to infinity at infinite sets.

### Absolute continuity #

We say that `μ`

is absolutely continuous with respect to `ν`

, or that `μ`

is dominated by `ν`

,
if `ν(A) = 0`

implies that `μ(A) = 0`

.

**Alias** of `absolutely_continuous_of_le`

.

**Alias** of `absolutely_continuous_of_eq`

.

**Alias** of `ae_le_iff_absolutely_continuous`

.

**Alias** of `ae_le_iff_absolutely_continuous`

.

**Alias** of `absolutely_continuous.ae_le`

.

### Quasi measure preserving maps (a.k.a. non-singular maps) #

- measurable : measurable f
- absolutely_continuous : ⇑(measure_theory.measure.map f) μa ≪ μb

A map `f : α → β`

is said to be *quasi measure preserving* (a.k.a. non-singular) w.r.t. measures
`μa`

and `μb`

if it is measurable and `μb s = 0`

implies `μa (f ⁻¹' s) = 0`

.

### The `cofinite`

filter #

The filter of sets `s`

such that `sᶜ`

has finite measure.

### Mutually singular measures #

Two measures `μ`

, `ν`

are said to be mutually singular if there exists a measurable set `s`

such that `μ s = 0`

and `ν sᶜ = 0`

.

A version of the **Borel-Cantelli lemma**: if `pᵢ`

is a sequence of predicates such that
`∑ μ {x | pᵢ x}`

is finite, then the measure of `x`

such that `pᵢ x`

holds frequently as `i → ∞`

(or
equivalently, `pᵢ x`

holds for infinitely many `i`

) is equal to zero.

A version of the **Borel-Cantelli lemma**: if `sᵢ`

is a sequence of sets such that
`∑ μ sᵢ`

exists, then for almost all `x`

, `x`

does not belong to almost all `sᵢ`

.

A measure `μ`

is called finite if `μ univ < ∞`

.

## Instances

- measure_theory.is_finite_measure_of_is_empty
- measure_theory.is_probability_measure.to_is_finite_measure
- measure_theory.restrict.is_finite_measure
- measure_theory.is_finite_measure_zero
- measure_theory.is_finite_measure_add
- measure_theory.is_finite_measure_smul_nnreal
- measure_theory.measure.is_finite_measure_sub
- measure_theory.is_finite_measure_trim
- real.is_finite_measure_restrict_Icc
- real.is_finite_measure_restrict_Ico
- real.is_finite_measure_restrict_Ioc
- real.is_finite_measure_restrict_Ioo
- measure_theory.signed_measure.to_measure_of_zero_le_finite
- measure_theory.signed_measure.to_measure_of_le_zero_finite
- measure_theory.jordan_decomposition.pos_part_finite
- measure_theory.jordan_decomposition.neg_part_finite
- measure_theory.measure.singular_part.measure_theory.is_finite_measure
- measure_theory.measure.with_density.measure_theory.is_finite_measure
- measure_theory.measure.restrict.measure_theory.is_finite_measure
- measure_theory.finite_measure.is_finite_measure

The measure of the whole space with respect to a finite measure, considered as `ℝ≥0`

.

## Equations

`le_of_add_le_add_left`

is normally applicable to `ordered_cancel_add_comm_monoid`

,
but it holds for measures with the additional assumption that μ is finite.

A measure `μ`

is called a probability measure if `μ univ = 1`

.

Measure `μ`

*has no atoms* if the measure of each singleton is zero.

NB: Wikipedia assumes that for any measurable set `s`

with positive `μ`

-measure,
there exists a measurable `t ⊆ s`

such that `0 < μ t < μ s`

. While this implies `μ {x} = 0`

,
the converse is not true.

A measure is called finite at filter `f`

if it is finite at some set `s ∈ f`

.
Equivalently, it is eventually finite at `s`

in `f.lift' powerset`

.

- set : ℕ → set α
- set_mem : ∀ (i : ℕ), self.set i ∈ C
- finite : ∀ (i : ℕ), ⇑μ (self.set i) < ⊤
- spanning : (⋃ (i : ℕ), self.set i) = set.univ

`μ`

has finite spanning sets in `C`

if there is a countable sequence of sets in `C`

that have
finite measures. This structure is a type, which is useful if we want to record extra properties
about the sets, such as that they are monotone.
`sigma_finite`

is defined in terms of this: `μ`

is σ-finite if there exists a sequence of
finite spanning sets in the collection of all measurable sets.

- out' : nonempty (μ.finite_spanning_sets_in set.univ)

A measure `μ`

is called σ-finite if there is a countable collection of sets
`{ A i | i ∈ ℕ }`

such that `μ (A i) < ∞`

and `⋃ i, A i = s`

.

## Instances

- measure_theory.is_finite_measure.to_sigma_finite
- measure_theory.sigma_finite_of_locally_finite
- measure_theory.measure.regular.sigma_finite
- measure_theory.measure.is_haar_measure.sigma_finite
- measure_theory.measure.is_add_haar_measure.sigma_finite
- measure_theory.restrict.sigma_finite
- measure_theory.sum.sigma_finite
- measure_theory.add.sigma_finite
- measure_theory.measure.prod.sigma_finite
- measure_theory.measure.sigma_finite_tprod
- measure_theory.measure.pi.sigma_finite
- measure_theory.measure.sigma_finite_haar_measure
- measure_theory.measure.sigma_finite_add_haar_measure
- measure_theory.ae_fin_strongly_measurable.sigma_finite_restrict
- measure_theory.measure.singular_part.measure_theory.sigma_finite
- measure_theory.measure.with_density.measure_theory.sigma_finite

If `μ`

is σ-finite it has finite spanning sets in the collection of all measurable sets.

A noncomputable way to get a monotone collection of sets that span `univ`

and have finite
measure using `classical.some`

. This definition satisfies monotonicity in addition to all other
properties in `sigma_finite`

.

## Equations

`spanning_sets_index μ x`

is the least `n : ℕ`

such that `x ∈ spanning_sets μ n`

.

## Equations

If `μ`

has finite spanning sets in `C`

and `C ∩ {s | μ s < ∞} ⊆ D`

then `μ`

has finite spanning
sets in `D`

.

If `μ`

has finite spanning sets in `C`

and `C ⊆ D`

then `μ`

has finite spanning sets in `D`

.

If `μ`

has finite spanning sets in the collection of measurable sets `C`

, then `μ`

is σ-finite.

An extensionality for measures. It is `ext_of_generate_from_of_Union`

formulated in terms of
`finite_spanning_sets_in`

.

Given measures `μ`

, `ν`

where `ν ≤ μ`

, `finite_spanning_sets_in.of_le`

provides the induced
`finite_spanning_set`

with respect to `ν`

from a `finite_spanning_set`

with respect to `μ`

.

Every finite measure is σ-finite.

- finite_at_nhds : ∀ (x : α), μ.finite_at_filter (𝓝 x)

A measure is called locally finite if it is finite in some neighborhood of each point.

If a set has zero measure in a neighborhood of each of its points, then it has zero measure in a second-countable space.