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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.

Definition

Let ${\displaystyle \zeta }$ be a random measure on the measurable space ${\displaystyle (S,{\mathcal {A}})}$ and denote the expected value of a random element ${\displaystyle Y}$ with ${\displaystyle \operatorname {E} }$.

The intensity measure

${\displaystyle \operatorname {E} \zeta \colon {\mathcal {A}}\to }$

of ${\displaystyle \zeta }$ is defined as

${\displaystyle \operatorname {E} \zeta (A)=\operatorname {E} }$

for all ${\displaystyle A\in {\mathcal {A}}}$.

Note the difference in notation between the expectation value of a random element ${\displaystyle Y}$, denoted by ${\displaystyle \operatorname {E} }$ and the intensity measure of the random measure ${\displaystyle \zeta }$, denoted by ${\displaystyle \operatorname {E} \zeta }$.

Properties

The intensity measure ${\displaystyle \operatorname {E} \zeta }$ is always s-finite and satisfies

${\displaystyle \operatorname {E} \left=\int f(x)\operatorname {E} \zeta (dx)}$

for every positive measurable function ${\displaystyle f}$ on ${\displaystyle (S,{\mathcal {A}})}$.

References

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. ^ 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.

• Measures (measure theory)
• Probability theory