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Every nonnegative measurable function is the increasing limit of simple measurable functions
Statement
Let be measurable. Then there is an increasing sequence of nonnegative simple measurable functions such that for every .
Facts & Assumptions
Given: A measurable function .
For a measurable , the sets are measurable for every Borel (Extended-real-valued measurable functions).
A nonnegative measurable function with finite range is a nonnegative simple measurable function (Nonnegative simple measurable functions).
Increasing pointwise suprema of measurable functions are measurable, and measurable functions remain measurable under the elementary truncations used below (Closure properties of measurable functions used by the integral).
Proof
Set . For and , put and set Each and is measurable by [L1], the range of is finite, and therefore each is simple by [L2]; is also simple.
For each , one has . If and , then ; if , then . Hence .
The functions are increasing. Indeed, is the largest multiple of at most , hence is also a multiple of at most . The maximality of the latter dyadic truncation gives . Together with step 2.1, this proves .
Depends on
Used by
- Additivity of the nonnegative Lebesgue integral Corollary
- Eigenfunction for a probability system Definition
- The one-dimensional torus and its normalized Haar integral Definition
- Integrating against a Dirac measure is evaluation at the point Example
- Multiplication operators: domain, spectral measure and spectrum Example
- Point evaluation is represented by a Dirac measure Example
- Bounded-function form of the Markov property Lemma
- Conditioning a known state and independent noise Lemma
- Conditioning a known variable and an independent variable Lemma
- Kernel composition is well defined and associative Lemma
- Simultaneous rational conditional distribution function versions Lemma
- Two common diagonalizations differ by a bimeasurable base isomorphism and a measurable field of unitaries Lemma
- Haar integration is translation and conjugation invariant Proposition
- Change of variables for expectation Theorem
- Conditional integration through a regular conditional law Theorem
- Density of elementary predictable processes in predictable L2 Theorem
- Disintegration of a joint law on standard borel spaces Theorem
- Fekete–Szegő equality of logarithmic capacity, transfinite diameter, and Chebyshev constant Theorem
- First-step equations for nonnegative exit costs Theorem
- Future-path Markov property Theorem
- Integrable simple functions are dense in L¹(μ) Theorem
- Integral invariance under measure-preserving maps Theorem
- Integrating against a density agrees with integrating the product Theorem
- Irreducible direct integral decomposition for type I groups Theorem
- Krylov–Bogolyubov existence of an invariant probability Theorem
- Markov property for bounded future path functionals Theorem
- Measurability of integration against a kernel Theorem
- Measurable integration extends smooth density integration Theorem
- Monotone convergence for the integral Theorem
- Positive harmonic boundary measures and compact normalized families Theorem
- Spectral multiplicity model for separably acting abelian von Neumann algebras Theorem
- Strong Markov property of Brownian motion Theorem
- Taking out what is known Theorem
Dependency tree · two levels
9 results within two dependency steps of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.
Sources
- Richard F. Bass, Real Analysis for Graduate Students, Theorem 7.1 setup (standard reference, not scraped)
- John K. Hunter, Measure Theory Notes, Definition 4.4 (standard reference, not scraped)