IDEAS home Printed from https://ideas.repec.org/a/eee/spapps/v130y2020i6p3499-3539.html
   My bibliography  Save this article

Convergence of metric two-level measure spaces

Author

Listed:
  • Meizis, Roland

Abstract

We extend the notion of metric measure spaces to so-called metric two-level measure spaces (m2m spaces): An m2m space (X,r,ν) is a Polish metric space (X,r) equipped with a two-level measure ν∈Mf(Mf(X)), i.e. a finite measure on the set of finite measures on X. We introduce a topology on the set of (equivalence classes of) m2m spaces induced by certain test functions (i.e. the initial topology with respect to these test functions) and show that this topology is Polish by providing a complete metric. The framework introduced in this article is motivated by possible applications in biology. It is well suited for modeling the random evolution of the genealogy of a population in a hierarchical system with two levels, for example, host–parasite systems or populations which are divided into colonies. As an example we apply our theory to construct a random m2m space modeling the genealogy of a nested Kingman coalescent.

Suggested Citation

  • Meizis, Roland, 2020. "Convergence of metric two-level measure spaces," Stochastic Processes and their Applications, Elsevier, vol. 130(6), pages 3499-3539.
  • Handle: RePEc:eee:spapps:v:130:y:2020:i:6:p:3499-3539
    DOI: 10.1016/j.spa.2019.10.002
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304414918301893
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.spa.2019.10.002?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:spapps:v:130:y:2020:i:6:p:3499-3539. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/505572/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.