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Probability Theory: A Comprehensive Course

Achim Klenke

Chapter 24

The Poisson Point Process - all with Video Answers

Educators


Section 1

Random Measures

01:51

Problem 1

Let $X_{1}, X_{2}, \ldots$ be random measures and $\lambda_{1}, \lambda_{2}, \ldots \in[0, \infty) .$ Define $X:=\sum_{n=1}^{\infty} \lambda_{n} X_{n} .$ Show that $X$ is a random measure if and only if we have $\mathbf{P}[X(B)<\infty]=1$ for all $B \in \mathcal{B}_{b}(E) .$ Infer that if $X$ is a random variable with values in $(\tilde{\mathcal{M}}(E), \tilde{\mathbb{M}}(E))$ and $\mathbf{E}[X] \in \mathcal{M}(E)$, then $X$ is a random measure.

Hunza Gilgit
Hunza Gilgit
Numerade Educator
02:19

Problem 2

Let $\tau_{w}$ be the topology of weak convergence on $\mathcal{M}_{1}(E)$ and let $\sigma\left(\tau_{w}\right)$ be the Borel $\sigma$-algebra on $\mathcal{M}_{1}(E) .$ Show that $\left.\mathbb{M}\right|_{\mathcal{M}_{1}(E)}=\sigma\left(\tau_{w}\right)$.

Nick Johnson
Nick Johnson
Numerade Educator