ArticleslgStudy

science

Regular measure

Regular measure is a science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Regular measure rather than just read about it. In short: In mathematics, a regular measure on a topological space is a measure for which every measurable set can be approximated from above by open measurable sets and from below by compact measurable sets. Definition Let (X, T) be a topological space and let Σ be a σ-algebra on X.

Key takeaways

  • Regular measure belongs to science; place it in that map before memorising details.
  • Learn the definition first, then one example that makes the definition concrete.
  • Connect Regular measure to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of Regular measure from memory before moving on to harder problems.

Reference excerpt

In mathematics, a regular measure on a topological space is a measure for which every measurable set can be approximated from above by open measurable sets and from below by compact measurable sets.

Definition Let (X, T) be a topological space and let Σ be a σ-algebra on X. Let μ be a measure on (X, Σ). A measurable subset A of X is said to be inner regular if

μ ( A ) = sup { μ ( F ) ∣ F ⊆ A , F compact and measurable } {\displaystyle \mu (A)=\sup\{\mu (F)\mid F\subseteq A,F{\text{ compact and measurable}}\}}

This property is sometimes referred to in words as "approximation from within by compact sets." Some authors use the term tight as a synonym for inner regular. This use of the term is closely related to tightness of a family of measures, since a finite measure μ is inner regular if and only if, for all ε>0, there is some compact subset K of X such that μ(X\K)<ε. This is precisely the condition that the singleton collection of measures {μ} is tight. A measurable subset A is said to be outer regular if

μ ( A ) = inf { μ ( G ) ∣ G ⊇ A , G open and measurable } {\displaystyle \mu (A)=\inf\{\mu (G)\mid G\supseteq A,G{\text{ open and measurable}}\}}

It is said to be regular if it is both inner and outer regular. If these properties are verified for every measurable subsets, then the measure is also said to be (inner/outer) regular.

Examples

Regular measures The Lebesgue measure on the real line is a regular measure: see the regularity theorem for Lebesgue measure. Any Baire probability measure on any locally compact σ-compact Hausdorff space is a regular measure. Any Borel probability measure on a locally compact Hausdorff space with a countable base for its topology, or compact metric space, or Radon space, is regular.

Inner regular measures that are not outer regular An example of a measure on the real line with its usual topology that is not outer regular is the measure μ {\displaystyle \mu } where μ ( ∅ ) = 0 {\displaystyle \mu (\emptyset )=0} , μ ( { 1 } ) = 0 {\displaystyle \mu \left(\{1\}\right)=0\,\,} , and μ ( A ) = ∞ {\displaystyle \mu (A)=\infty \,\,} for any other set A {\displaystyle A} . The Borel measure on the plane that assigns to any Borel set the sum of the (1-dimensional) measures of its horizontal sections is inner regular but not outer regular, as every non-empty open set has infinite measure. A variation of this example is a disjoint union of an uncountable number of copies of the real line with Lebesgue measure. An example of a Borel measure μ {\displaystyle \mu } on a locally compact Hausdorff space that is inner regular, σ-finite, and locally finite but not outer regular is given by Bourbaki (2004, Chapter IV, Exercise 5 of section 1) as follows. The topological space X {\displaystyle X} has as underlying set the subset of the real plane given by the y-axis { 0 } × R {\displaystyle \{0\}\times \mathbb {R} } together with the points (1/n,m/n2) with m,n positive integers. The topology is given as follows. The single points (1/n,m/n2) are all open sets. A base of neighborhoods of the point (0,y) is given by wedges consisting of all points in X of the form (u,v) with |v − y| ≤ |u| ≤ 1/n for a positive integer n. This space X is locally compact. The measure μ is given by letting the y-axis have measure 0 and letting the point (1/n,m/n2) have measure 1/n3. This measure is inner regular and locally finite, but is not outer regular as any open set containing the y-axis has measure infinity.

Outer regular measures that are not inner regular If μ is the inner regular measure in the previous example, and M is the measure given by M(S) = infU⊇S μ(U) where the inf is taken over all open sets containing the Borel set S, then M is an outer regular locally finite Borel measure on a locally compact Hausdorff space that is not inner regular in the strong sense, though all open sets are inner regular so it is inner regular in the weak sense. The measures M and μ coincide on all open sets, all compact sets, and all sets on which M has finite measure. The y-axis has infinite M-measure though all compact subsets of it have measure 0. A measurable cardinal with the discrete topology has a Borel probability measure such that every compact subset has measure 0, so this measure is outer regular but not inner regular. The existence of measurable cardinals cannot be proved in ZF set theory but (as of 2013) is thought to be consistent with it.

Measures that are neither inner nor outer regular The space of all ordinals at most equal to the first uncountable ordinal Ω, with the topology generated by open intervals, is a compact Hausdorff space. The measure that assigns measure 1 to Borel sets containing an unbounded closed subset of the countable ordinals and assigns 0 to other Borel sets is a Borel probability measure that is neither inner regular nor outer regular.

See also Borel regular measure Radon measure Regularity theorem for Lebesgue measure

References

Bibliography Billingsley, Patrick (1999). Convergence of Probability Measures. New York: John Wiley & Sons, Inc. ISBN 0-471-19745-9. Bourbaki, Nicolas (2004). Integration I. Springer-Verlag. ISBN 3-540-41129-1. Parthasarathy, K. R. (2005). Probability measures on metric spaces. AMS Chelsea Publishing, Providence, RI. p. xii+276. ISBN 0-8218-3889-X. MR 2169627 (See chapter 2) Dudley, R. M. (1989). Real Analysis and Probability. Chapman & Hall.

Worked examples

Example 1 — a first encounter with Regular measure

Start with the simplest possible case. Write down what Regular measure claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Regular measure before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Regular measure ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Regular measure

In research
Regular measure appears in science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry
Engineering practice reuses Regular measure in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom
Regular measure is common in secondary-school and first-year university syllabi. It links to neighbouring topics Measures (measure theory), so understanding it makes those chapters shorter.
In everyday life
Look for Regular measure outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

Affiliate

Preply — study more efficiently by working with a personal tutor. 50% off.

How to study Regular measure in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Regular measure means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain Regular measure out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Regular measure in simple terms?

In mathematics, a regular measure on a topological space is a measure for which every measurable set can be approximated from above by open measurable sets and from below by compact measurable sets. Definition Let (X, T) be a topological space and let Σ be a σ-algebra on X.

Why does Regular measure matter?

Because it connects several science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Regular measure?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Regular measure.

Tags

  • Measures (measure theory)

Keep exploring