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Intensity measure

In probability theory, an intensity measure is a measure that is derived from a random measure. The intensity measure is a non-random measure and is defined as the expectation value of the random measure of a set, hence it corresponds to the average volume the random measure assigns to a set. The intensity measure contains important information about the properties of the random measure. A Poisson point process, interpreted as a random measure, is for example uniquely determined by its intensity measure. [1]

Definition edit

Let   be a random measure on the measurable space   and denote the expected value of a random element   with  .

The intensity measure

 

of   is defined as

 

for all  .[2] [3]

Note the difference in notation between the expectation value of a random element  , denoted by   and the intensity measure of the random measure  , denoted by  .

Properties edit

The intensity measure   is always s-finite and satisfies

 

for every positive measurable function   on  .[3]

References edit

  1. ^ Klenke, Achim (2008). Probability Theory. Berlin: Springer. p. 528. doi:10.1007/978-1-84800-048-3. ISBN 978-1-84800-047-6.
  2. ^ Klenke, Achim (2008). Probability Theory. Berlin: Springer. p. 526. doi:10.1007/978-1-84800-048-3. ISBN 978-1-84800-047-6.
  3. ^ a b Kallenberg, Olav (2017). Random Measures, Theory and Applications. Switzerland: Springer. p. 53. doi:10.1007/978-3-319-41598-7. ISBN 978-3-319-41596-3.

intensity, measure, probability, theory, intensity, measure, measure, that, derived, from, random, measure, intensity, measure, random, measure, defined, expectation, value, random, measure, hence, corresponds, average, volume, random, measure, assigns, intens. In probability theory an intensity measure is a measure that is derived from a random measure The intensity measure is a non random measure and is defined as the expectation value of the random measure of a set hence it corresponds to the average volume the random measure assigns to a set The intensity measure contains important information about the properties of the random measure A Poisson point process interpreted as a random measure is for example uniquely determined by its intensity measure 1 Definition editLet z displaystyle zeta nbsp be a random measure on the measurable space S A displaystyle S mathcal A nbsp and denote the expected value of a random element Y displaystyle Y nbsp with E Y displaystyle operatorname E Y nbsp The intensity measure E z A 0 displaystyle operatorname E zeta colon mathcal A to 0 infty nbsp of z displaystyle zeta nbsp is defined as E z A E z A displaystyle operatorname E zeta A operatorname E zeta A nbsp for all A A displaystyle A in mathcal A nbsp 2 3 Note the difference in notation between the expectation value of a random element Y displaystyle Y nbsp denoted by E Y displaystyle operatorname E Y nbsp and the intensity measure of the random measure z displaystyle zeta nbsp denoted by E z displaystyle operatorname E zeta nbsp Properties editThe intensity measure E z displaystyle operatorname E zeta nbsp is always s finite and satisfies E f x z d x f x E z d x displaystyle operatorname E left int f x zeta mathrm d x right int f x operatorname E zeta dx nbsp for every positive measurable function f displaystyle f nbsp on S A displaystyle S mathcal A nbsp 3 References edit Klenke Achim 2008 Probability Theory Berlin Springer p 528 doi 10 1007 978 1 84800 048 3 ISBN 978 1 84800 047 6 Klenke Achim 2008 Probability Theory Berlin Springer p 526 doi 10 1007 978 1 84800 048 3 ISBN 978 1 84800 047 6 a b Kallenberg Olav 2017 Random Measures Theory and Applications Switzerland Springer p 53 doi 10 1007 978 3 319 41598 7 ISBN 978 3 319 41596 3 Retrieved from https en wikipedia org w index php title Intensity measure amp oldid 1061676741, wikipedia, wiki, book, books, library,

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