2013•arXiv (Cornell University)Open access

A moment problem for random discrete measures

Yuri Kondratiev, Tobias Kuna, Eugene Lytvynov

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Abstract

Let $X$ be a locally compact Polish space. A random measure on $X$ is a probability measure on the space of all (nonnegative) Radon measures on $X$. Denote by $\mathbb K(X)$ the cone of all Radon measures $η$ on $X$ which are of the form $η=\sum_{i}s_iδ_{x_i}$, where, for each $i$, $s_i>0$ and $δ_{x_i}$ is the Dirac measure at $x_i\in X$. A random discrete measure on $X$ is a probability measure on $\mathbb K(X)$. The main result of the paper states a necessary and sufficient condition (conditional upon a mild a priori bound) when a random measure $μ$ is also a random discrete measure. This condition is formulated solely in terms of moments of the random measure $μ$. Classical examples of random discrete measures are completely random measures and additive subordinators, however, the main result holds independently of any independence property. As a corollary, a characterisation via a moments is given when a random measure is a point process.

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Let $X$ be a locally compact Polish space. A random measure on $X$ is a probability measure on the space of all (nonnegative) Radon measures on $X$. Denote by $\mathbb K(X)$ the cone of all Radon measures $η$ on $X$ which are of the form $η=\sum_{i}s_iδ_{x_i}$, where, for each $i$, $s_i>0$ and $δ_{x_i}$ is the Dirac measure at $x_i\in X$. A random discrete measure on $X$ is a probability measure on $\mathbb K(X)$. The main result of the paper states a necessary and sufficient condition (conditional upon a mild a priori bound) when a random measure $μ$ is also a random discrete measure. This condition is formulated solely in terms of moments of the random measure $μ$. Classical examples of random discrete measures are completely random measures and additive subordinators, however, the main result holds independently of any independence property. As a corollary, a characterisation via a moments is given when a random measure is a point process.

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Available abstract

Let $X$ be a locally compact Polish space. A random measure on $X$ is a probability measure on the space of all (nonnegative) Radon measures on $X$. Denote by $\mathbb K(X)$ the cone of all Radon measures $η$ on $X$ which are of the form $η=\sum_{i}s_iδ_{x_i}$, where, for each $i$, $s_i>0$ and $δ_{x_i}$ is the Dirac measure at $x_i\in X$. A random discrete measure on $X$ is a probability measure on $\mathbb K(X)$. The main result of the paper states a necessary and sufficient condition (conditional upon a mild a priori bound) when a random measure $μ$ is also a random discrete measure. This condition is formulated solely in terms of moments of the random measure $μ$. Classical examples of random discrete measures are completely random measures and additive subordinators, however, the main result holds independently of any independence property. As a corollary, a characterisation via a moments is given when a random measure is a point process.

Key concepts: Measure (data warehouse), Random measure, Point process, Mathematics, Probability measure, Random element, Discrete measure, Moment (physics)

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