S-finite measure

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Template:Lowercase title In measure theory, a branch of mathematics that studies generalized notions of volumes, an s-finite measure is a special type of measure. An s-finite measure is more general than a finite measure, but allows one to generalize certain proofs for finite measures.

The s-finite measures should not be confused with the σ-finite (sigma-finite) measures.

Definition

Let (X,𝒜) be a measurable space and μ a measure on this measurable space. The measure μ is called an s-finite measure, if it can be written as a countable sum of finite measures νn (n),[1]

μ=n=1νn.

Example

The Lebesgue measure λ is an s-finite measure. For this, set

Bn=(n,n+1][n1,n)

and define the measures νn by

νn(A)=λ(ABn)

for all measurable sets A. These measures are finite, since νn(A)νn(Bn)=2 for all measurable sets A, and by construction satisfy

λ=n=1νn.

Therefore the Lebesgue measure is s-finite.

Properties

Relation to σ-finite measures

Every σ-finite measure is s-finite, but not every s-finite measure is also σ-finite.

To show that every σ-finite measure is s-finite, let μ be σ-finite. Then there are measurable disjoint sets B1,B2, with μ(Bn)< and

n=1Bn=X

Then the measures

νn():=μ(Bn)

are finite and their sum is μ. This approach is just like in the example above.

An example for an s-finite measure that is not σ-finite can be constructed on the set X={a} with the σ-algebra 𝒜={{a},}. For all n, let νn be the counting measure on this measurable space and define

μ:=n=1νn.

The measure μ is by construction s-finite (since the counting measure is finite on a set with one element). But μ is not σ-finite, since

μ({a})=n=1νn({a})=n=11=.

So μ cannot be σ-finite.

Equivalence to probability measures

For every s-finite measure μ=n=1νn, there exists an equivalent probability measure P, meaning that μP.[1] One possible equivalent probability measure is given by

P=n=12nνnνn(X).

References

Template:Measure theory