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Finite modal velocity fields inherit finite second moments
Statement
Assume the Axiom of Choice (AC), meaning every family of nonempty sets has a choice function. Let , , and be as in the Definition of the finite-second-moment random velocity sensor law, with . Let , choose , and let be a probability space. Suppose is measurable and . Define by , set , and define for every . Then is strongly measurable, is a probability measure on , and
Statement references
Finite-second-moment random velocity sensor law
Probability measures and probability spaces
Strongly measurable Banach-valued function
Riesz-Fischer completeness of for
The nonnegative Lebesgue integral
Every nonnegative measurable function is the increasing limit of simple measurable functions
Monotone convergence for the integral
Tao, An Introduction to Measure Theory, Section 1.4, Exercise 1.4.38 and Theorem 1.4.44
Facts & Assumptions
Given: A finite measure space with , , vectors , a probability space , and a measurable with .
Assume AC, meaning every family of nonempty sets has a choice function. It supplies countable choice for the scalar completeness result used below. The Axiom of Choice
The Definition identifies isometrically with the finite direct sum of scalar spaces, with , its norm-Borel sigma-algebra , and the finite-second-moment law defined by the norm-square integral. Finite-second-moment random velocity sensor law
A probability measure is countably additive and has total mass one. Probability measures and probability spaces
Strong measurability means being an almost-everywhere pointwise norm limit of measurable simple Banach-valued functions. Strongly measurable Banach-valued function
The nonnegative integral is the supremum over nonnegative simple minorants; the admissible set contains the zero function. The nonnegative Lebesgue integral
Every nonnegative measurable function is an increasing pointwise limit of nonnegative simple measurable functions. Every nonnegative measurable function is the increasing limit of simple measurable functions
For a nondecreasing sequence of nonnegative measurable functions, the integrals increase to the integral of the pointwise limit. Monotone convergence for the integral
The Euclidean inner product on satisfies . Cauchy-Schwarz with its equality case, the triangle inequality for , the parallelogram law and polarisation
Under countable choice, every Cauchy sequence in scalar converges in its quotient norm; AC suffices for the theorem's countable-choice hypothesis, which is used there to select countably many measurable representatives. Riesz-Fischer completeness of for The Axiom of Choice
Proof
Given: The hypotheses and notation of the Statement.
A real Banach space means a real normed linear space complete in its norm metric. This is the codomain condition in [F3] for the phrase “strongly measurable.” Write each in its scalar coordinate classes ; the Definition gives the isometric finite direct-sum norm . If is Cauchy in this norm, meaning that for each there is such that for all , then each coordinate sequence is Cauchy because . By [F8], each coordinate sequence converges to a class . The finite tuple belongs to , and the finite sum of squared coordinate errors tends to zero, so in . Hence is complete and normed, so it is a real Banach space and [F3] applies.
Put . For , the triangle inequality and [F7] give Thus is continuous and is Borel measurable. For each , truncate every coordinate of to and round it down to a multiple of , obtaining a finite-valued measurable map with pointwise. The maps are measurable simple -valued functions and converge pointwise in norm to by continuity of . Hence is strongly measurable by [F3].
For Borel sets , the preimage map preserves complements and countable disjoint unions, and . Therefore is countably additive with by [F2]. For any nonnegative Borel function , choose nonnegative simple functions as provided by [F5]. The identity follows by writing each simple integral as a finite sum over its level sets and using the definition of . Apply [F6] to both sides and pass to the increasing limits to obtain . Apply this identity to the continuous function . The bound in step 1.2 with gives , so . This proves the stated moment identity and bound, and hence that is a finite-second-moment law of the Definition.
Depends on
Finite-second-moment random velocity sensor law
Probability measures and probability spaces
Strongly measurable Banach-valued function
Riesz-Fischer completeness of for
The Axiom of Choice
The nonnegative Lebesgue integral
Every nonnegative measurable function is the increasing limit of simple measurable functions
Monotone convergence for the integral
Cauchy-Schwarz with its equality case, the triangle inequality for , the parallelogram law and polarisation