Journal article 1190 views
A moment problem for random discrete measures
Stochastic Processes and their Applications, Volume: 125, Issue: 9, Pages: 3541 - 3569
Swansea University Author: Eugene Lytvynov
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DOI (Published version): 10.1016/j.spa.2015.03.007
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 $\eta$ on $X$ which are of the form $\eta=\sum_{i}s_i\delta_{x_i}$, where, for each $i$, $s_i>...
Published in: | Stochastic Processes and their Applications |
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2015
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URI: | https://cronfa.swan.ac.uk/Record/cronfa22142 |
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2019-08-06T08:41:50.8446144 v2 22142 2015-06-22 A moment problem for random discrete measures e5b4fef159d90a480b1961cef89a17b7 0000-0001-9685-7727 Eugene Lytvynov Eugene Lytvynov true false 2015-06-22 MACS 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 $\eta$ on $X$ which are of the form $\eta=\sum_{i}s_i\delta_{x_i}$, where, for each $i$, $s_i>0$ and $\delta_{x_i}$ 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 {\it a priori\/} bound) when a random measure $\mu$ is also a random discrete measure. This condition is formulated solely in terms of moments of the random measure $\mu$. 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. Journal Article Stochastic Processes and their Applications 125 9 3541 3569 Discrete random measure, moment problem, point process, random measure. 31 12 2015 2015-12-31 10.1016/j.spa.2015.03.007 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University 2019-08-06T08:41:50.8446144 2015-06-22T16:05:39.6348203 Faculty of Science and Engineering School of Mathematics and Computer Science - Mathematics Yuri G. Kondratiev 1 Tobias Kuna 2 Eugene Lytvynov 0000-0001-9685-7727 3 |
title |
A moment problem for random discrete measures |
spellingShingle |
A moment problem for random discrete measures Eugene Lytvynov |
title_short |
A moment problem for random discrete measures |
title_full |
A moment problem for random discrete measures |
title_fullStr |
A moment problem for random discrete measures |
title_full_unstemmed |
A moment problem for random discrete measures |
title_sort |
A moment problem for random discrete measures |
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e5b4fef159d90a480b1961cef89a17b7 |
author_id_fullname_str_mv |
e5b4fef159d90a480b1961cef89a17b7_***_Eugene Lytvynov |
author |
Eugene Lytvynov |
author2 |
Yuri G. Kondratiev Tobias Kuna Eugene Lytvynov |
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Journal article |
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Stochastic Processes and their Applications |
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125 |
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9 |
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3541 |
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2015 |
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Swansea University |
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10.1016/j.spa.2015.03.007 |
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Faculty of Science and Engineering |
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Faculty of Science and Engineering |
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facultyofscienceandengineering |
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Faculty of Science and Engineering |
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School of Mathematics and Computer Science - Mathematics{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Mathematics |
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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 $\eta$ on $X$ which are of the form $\eta=\sum_{i}s_i\delta_{x_i}$, where, for each $i$, $s_i>0$ and $\delta_{x_i}$ 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 {\it a priori\/} bound) when a random measure $\mu$ is also a random discrete measure. This condition is formulated solely in terms of moments of the random measure $\mu$. 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. |
published_date |
2015-12-31T18:46:50Z |
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1821522904376934400 |
score |
11.047674 |