On a representation of partially-distinguishable populations
On a representation of partially-distinguishable populations
A representation of heterogeneous stochastic populations that are composed of sub-populations with different levels of distinguishability is introduced together with an analysis of its properties. It is demonstrated that any instance of this representation where individuals are independent can be related to a point process on the set of probability measures on the individual state space. The introduction of the proposed representation is fully constructive which ensures the meaningfulness of the approach.
60A10, 62C10, distinguishability, point process, Stochastic population
23-45
Houssineau, Jeremie
54d4df9b-ceaa-456d-b668-63c617e6894a
Clark, Daniel E.
537f80e8-cbe6-41eb-b1d4-31af1f0e6393
2 January 2020
Houssineau, Jeremie
54d4df9b-ceaa-456d-b668-63c617e6894a
Clark, Daniel E.
537f80e8-cbe6-41eb-b1d4-31af1f0e6393
Houssineau, Jeremie and Clark, Daniel E.
(2020)
On a representation of partially-distinguishable populations.
Statistics, 54 (1), .
(doi:10.1080/02331888.2019.1699929).
Abstract
A representation of heterogeneous stochastic populations that are composed of sub-populations with different levels of distinguishability is introduced together with an analysis of its properties. It is demonstrated that any instance of this representation where individuals are independent can be related to a point process on the set of probability measures on the individual state space. The introduction of the proposed representation is fully constructive which ensures the meaningfulness of the approach.
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Accepted/In Press date: 28 November 2019
Published date: 2 January 2020
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Publisher Copyright:
© 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.
Keywords:
60A10, 62C10, distinguishability, point process, Stochastic population
Identifiers
Local EPrints ID: 475500
URI: http://eprints.soton.ac.uk/id/eprint/475500
ISSN: 0233-1888
PURE UUID: 8f05d3be-56e1-4140-a606-d7577a62f8b4
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Date deposited: 20 Mar 2023 17:45
Last modified: 16 Mar 2024 23:15
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Author:
Jeremie Houssineau
Author:
Daniel E. Clark
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