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Equivalence (measure theory)

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In mathematics, and specifically in measure theory, equivalence is a notion of two measures being qualitatively similar. Specifically, the two measures agree on which events have measure zero.

Definition

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Let μ {\displaystyle \mu } {\displaystyle \mu } and ν {\displaystyle \nu } {\displaystyle \nu } be two measures on the measurable space ( X , A ) , {\displaystyle (X,{\mathcal {A}}),} {\displaystyle (X,{\mathcal {A}}),} and let N μ := { A A μ ( A ) = 0 } {\displaystyle {\mathcal {N}}_{\mu }:=\{A\in {\mathcal {A}}\mid \mu (A)=0\}} {\displaystyle {\mathcal {N}}_{\mu }:=\{A\in {\mathcal {A}}\mid \mu (A)=0\}} and N ν := { A A ν ( A ) = 0 } {\displaystyle {\mathcal {N}}_{\nu }:=\{A\in {\mathcal {A}}\mid \nu (A)=0\}} {\displaystyle {\mathcal {N}}_{\nu }:=\{A\in {\mathcal {A}}\mid \nu (A)=0\}} be the sets of μ {\displaystyle \mu } {\displaystyle \mu }-null sets and ν {\displaystyle \nu } {\displaystyle \nu }-null sets, respectively. Then the measure ν {\displaystyle \nu } {\displaystyle \nu } is said to be absolutely continuous in reference to μ {\displaystyle \mu } {\displaystyle \mu } if and only if N ν N μ . {\displaystyle {\mathcal {N}}_{\nu }\supseteq {\mathcal {N}}_{\mu }.} {\displaystyle {\mathcal {N}}_{\nu }\supseteq {\mathcal {N}}_{\mu }.} This is denoted as ν μ . {\displaystyle \nu \ll \mu .} {\displaystyle \nu \ll \mu .}

The two measures are called equivalent if and only if μ ν {\displaystyle \mu \ll \nu } {\displaystyle \mu \ll \nu } and ν μ , {\displaystyle \nu \ll \mu ,} {\displaystyle \nu \ll \mu ,}[1] which is denoted as μ ν . {\displaystyle \mu \sim \nu .} {\displaystyle \mu \sim \nu .} That is, two measures are equivalent if they satisfy N μ = N ν . {\displaystyle {\mathcal {N}}_{\mu }={\mathcal {N}}_{\nu }.} {\displaystyle {\mathcal {N}}_{\mu }={\mathcal {N}}_{\nu }.}

Examples

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On the real line

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Define the two measures on the real line as μ ( A ) = A 1 [ 0 , 1 ] ( x ) d x {\displaystyle \mu (A)=\int _{A}\mathbf {1} _{[0,1]}(x)\mathrm {d} x} {\displaystyle \mu (A)=\int _{A}\mathbf {1} _{[0,1]}(x)\mathrm {d} x} ν ( A ) = A x 2 1 [ 0 , 1 ] ( x ) d x {\displaystyle \nu (A)=\int _{A}x^{2}\mathbf {1} _{[0,1]}(x)\mathrm {d} x} {\displaystyle \nu (A)=\int _{A}x^{2}\mathbf {1} _{[0,1]}(x)\mathrm {d} x} for all Borel sets A . {\displaystyle A.} {\displaystyle A.} Then μ {\displaystyle \mu } {\displaystyle \mu } and ν {\displaystyle \nu } {\displaystyle \nu } are equivalent, since all sets outside of [ 0 , 1 ] {\displaystyle [0,1]} {\displaystyle [0,1]} have μ {\displaystyle \mu } {\displaystyle \mu } and ν {\displaystyle \nu } {\displaystyle \nu } measure zero, and a set inside [ 0 , 1 ] {\displaystyle [0,1]} {\displaystyle [0,1]} is a μ {\displaystyle \mu } {\displaystyle \mu }-null set or a ν {\displaystyle \nu } {\displaystyle \nu }-null set exactly when it is a null set with respect to Lebesgue measure.

Abstract measure space

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Look at some measurable space ( X , A ) {\displaystyle (X,{\mathcal {A}})} {\displaystyle (X,{\mathcal {A}})} and let μ {\displaystyle \mu } {\displaystyle \mu } be the counting measure, so μ ( A ) = | A | , {\displaystyle \mu (A)=|A|,} {\displaystyle \mu (A)=|A|,} where | A | {\displaystyle |A|} {\displaystyle |A|} is the cardinality of the set a. So the counting measure has only one null set, which is the empty set. That is, N μ = { } . {\displaystyle {\mathcal {N}}_{\mu }=\{\varnothing \}.} {\displaystyle {\mathcal {N}}_{\mu }=\{\varnothing \}.} So by the second definition, any other measure ν {\displaystyle \nu } {\displaystyle \nu } is equivalent to the counting measure if and only if it also has just the empty set as the only ν {\displaystyle \nu } {\displaystyle \nu }-null set.

Supporting measures

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A measure μ {\displaystyle \mu } {\displaystyle \mu } is called a supporting measure of a measure ν {\displaystyle \nu } {\displaystyle \nu } if μ {\displaystyle \mu } {\displaystyle \mu } is σ {\displaystyle \sigma } {\displaystyle \sigma }-finite and ν {\displaystyle \nu } {\displaystyle \nu } is equivalent to μ . {\displaystyle \mu .} {\displaystyle \mu .}[2]

References

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  1. ^ Klenke, Achim (2008). Probability Theory. Berlin: Springer. p. 156. doi:10.1007/978-1-84800-048-3. ISBN 978-1-84800-047-6.
  2. ^ Kallenberg, Olav (2017). Random Measures, Theory and Applications. Switzerland: Springer. p. 21. doi:10.1007/978-3-319-41598-7. ISBN 978-3-319-41596-3.
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