Lower Lebesgue integral for ℝ≥0∞
-valued functions #
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We define the lower Lebesgue integral of an ℝ≥0∞
-valued function.
Notation #
We introduce the following notation for the lower Lebesgue integral of a function f : α → ℝ≥0∞
.
∫⁻ x, f x ∂μ
: integral of a functionf : α → ℝ≥0∞
with respect to a measureμ
;∫⁻ x, f x
: integral of a functionf : α → ℝ≥0∞
with respect to the canonical measurevolume
onα
;∫⁻ x in s, f x ∂μ
: integral of a functionf : α → ℝ≥0∞
over a sets
with respect to a measureμ
, defined as∫⁻ x, f x ∂(μ.restrict s)
;∫⁻ x in s, f x
: integral of a functionf : α → ℝ≥0∞
over a sets
with respect to the canonical measurevolume
, defined as∫⁻ x, f x ∂(volume.restrict s)
.
The lower Lebesgue integral of a function f
with respect to a measure μ
.
Equations
- measure_theory.lintegral μ f = ⨆ (g : measure_theory.simple_func α ennreal) (hf : ⇑g ≤ f), g.lintegral μ
In the notation for integrals, an expression like ∫⁻ x, g ‖x‖ ∂μ
will not be parsed correctly,
and needs parentheses. We do not set the binding power of r
to 0
, because then
∫⁻ x, f x = 0
will be parsed incorrectly.
For any function f : α → ℝ≥0∞
, there exists a measurable function g ≤ f
with the same
integral.
∫⁻ a in s, f a ∂μ
is defined as the supremum of integrals of simple functions
φ : α →ₛ ℝ≥0∞
such that φ ≤ f
. This lemma says that it suffices to take
functions φ : α →ₛ ℝ≥0
.
Monotone convergence theorem -- sometimes called Beppo-Levi convergence.
See lintegral_supr_directed
for a more general form.
Monotone convergence theorem -- sometimes called Beppo-Levi convergence. Version with ae_measurable functions.
Monotone convergence theorem expressed with limits
If f
has finite integral, then ∫⁻ x in s, f x ∂μ
is absolutely continuous in s
: it tends
to zero as μ s
tends to zero. This lemma states states this fact in terms of ε
and δ
.
If f
has finite integral, then ∫⁻ x in s, f x ∂μ
is absolutely continuous in s
: it tends
to zero as μ s
tends to zero.
The sum of the lower Lebesgue integrals of two functions is less than or equal to the integral of their sum. The other inequality needs one of these functions to be (a.e.-)measurable.
If f g : α → ℝ≥0∞
are two functions and one of them is (a.e.) measurable, then the Lebesgue
integral of f + g
equals the sum of integrals. This lemma assumes that f
is integrable, see also
measure_theory.lintegral_add_right
and primed versions of these lemmas.
If f g : α → ℝ≥0∞
are two functions and one of them is (a.e.) measurable, then the Lebesgue
integral of f + g
equals the sum of integrals. This lemma assumes that g
is integrable, see also
measure_theory.lintegral_add_left
and primed versions of these lemmas.
A version of Markov's inequality for two functions. It doesn't follow from the standard
Markov's inequality because we only assume measurability of g
, not f
.
Markov's inequality also known as Chebyshev's first inequality.
Markov's inequality also known as Chebyshev's first inequality. For a version assuming
ae_measurable
, see mul_meas_ge_le_lintegral₀
.
Markov's inequality also known as Chebyshev's first inequality.
Weaker version of the monotone convergence theorem
Monotone convergence theorem for nonincreasing sequences of functions
Monotone convergence theorem for nonincreasing sequences of functions
Known as Fatou's lemma, version with ae_measurable
functions
Known as Fatou's lemma
Dominated convergence theorem for nonnegative functions
Dominated convergence theorem for nonnegative functions which are just almost everywhere measurable.
Dominated convergence theorem for filters with a countable basis
Monotone convergence for a supremum over a directed family and indexed by a countable type
Monotone convergence for a supremum over a directed family and indexed by a countable type.
If g : α → β
is a measurable embedding and f : β → ℝ≥0∞
is any function (not necessarily
measurable), then ∫⁻ a, f a ∂(map g μ) = ∫⁻ a, f (g a) ∂μ
. Compare with lintegral_map
wich
applies to any measurable g : α → β
but requires that f
is measurable as well.
The lintegral
transforms appropriately under a measurable equivalence g : α ≃ᵐ β
.
(Compare lintegral_map
, which applies to a wider class of functions g : α → β
, but requires
measurability of the function being integrated.)
Markov's inequality for the counting measure with hypothesis using tsum
in ℝ≥0∞
.
Markov's inequality for counting measure with hypothesis using tsum
in ℝ≥0
.
Lebesgue integral over finite and countable types and sets #
Given a measure μ : measure α
and a function f : α → ℝ≥0∞
, μ.with_density f
is the
measure such that for a measurable set s
we have μ.with_density f s = ∫⁻ a in s, f a ∂μ
.
Equations
- μ.with_density f = measure_theory.measure.of_measurable (λ (s : set α) (hs : measurable_set s), ∫⁻ (a : α) in s, f a ∂μ) _ _
Instances for measure_theory.measure.with_density
This is Exercise 1.2.1 from [Tao10]. It allows you to express integration of a measurable
function with respect to (μ.with_density f)
as an integral with respect to μ
, called the base
measure. μ
is often the Lebesgue measure, and in this circumstance f
is the probability density
function, and (μ.with_density f)
represents any continuous random variable as a
probability measure, such as the uniform distribution between 0 and 1, the Gaussian distribution,
the exponential distribution, the Beta distribution, or the Cauchy distribution (see Section 2.4
of [wasserman2004]). Thus, this method shows how to one can calculate expectations, variances,
and other moments as a function of the probability density function.
The Lebesgue integral of g
with respect to the measure μ.with_density f
coincides with
the integral of f * g
. This version assumes that g
is almost everywhere measurable. For a
version without conditions on g
but requiring that f
is almost everywhere finite, see
lintegral_with_density_eq_lintegral_mul_non_measurable
In a sigma-finite measure space, there exists an integrable function which is positive everywhere (and with an arbitrarily small integral).
If the Lebesgue integral of a function is bounded by some constant on all sets with finite
measure in a sub-σ-algebra and the measure is σ-finite on that sub-σ-algebra, then the integral
over the whole space is bounded by that same constant. Version for a measurable function.
See lintegral_le_of_forall_fin_meas_le'
for the more general ae_measurable
version.
If the Lebesgue integral of a function is bounded by some constant on all sets with finite measure in a sub-σ-algebra and the measure is σ-finite on that sub-σ-algebra, then the integral over the whole space is bounded by that same constant.
If the Lebesgue integral of a function is bounded by some constant on all sets with finite measure and the measure is σ-finite, then the integral over the whole space is bounded by that same constant.
A sigma-finite measure is absolutely continuous with respect to some finite measure.