Header

UZH-Logo

Maintenance Infos

A law of large numbers approximation for Markov population processes with countably many types


Barbour, A D; Luzcak, M J (2012). A law of large numbers approximation for Markov population processes with countably many types. Probability Theory and Related Fields, 153(3-4):727-757.

Abstract

When modelling metapopulation dynamics, the influence of a single patch on the metapopulation depends on the number of individuals in the patch. Since the population size has no natural upper limit, this leads to systems in which there are countably infinitely many possible types of individual. Analogous considerations apply in the transmission of parasitic diseases. In this paper, we prove a law of large numbers for quite general systems of this kind, together with a rather sharp bound on the rate of convergence in an appropriately chosen weighted ℓ 1 norm.

Abstract

When modelling metapopulation dynamics, the influence of a single patch on the metapopulation depends on the number of individuals in the patch. Since the population size has no natural upper limit, this leads to systems in which there are countably infinitely many possible types of individual. Analogous considerations apply in the transmission of parasitic diseases. In this paper, we prove a law of large numbers for quite general systems of this kind, together with a rather sharp bound on the rate of convergence in an appropriately chosen weighted ℓ 1 norm.

Statistics

Citations

Dimensions.ai Metrics
8 citations in Web of Science®
8 citations in Scopus®
Google Scholar™

Altmetrics

Downloads

178 downloads since deposited on 17 Feb 2012
12 downloads since 12 months
Detailed statistics

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Mathematics
Dewey Decimal Classification:510 Mathematics
Scopus Subject Areas:Physical Sciences > Analysis
Physical Sciences > Statistics and Probability
Social Sciences & Humanities > Statistics, Probability and Uncertainty
Language:English
Date:2012
Deposited On:17 Feb 2012 20:02
Last Modified:07 Dec 2023 02:42
Publisher:Springer
ISSN:0178-8051 (P) 1432-2064 (E)
Additional Information:The original publication is available at www.springerlink.com
OA Status:Green
Publisher DOI:https://doi.org/10.1007/s00440-011-0359-2
Related URLs:http://arxiv.org/abs/1001.0044
  • Content: Accepted Version
  • Description: Version 1
  • Content: Accepted Version
  • Description: Version 2
  • Content: Accepted Version
  • Description: Version 3
  • Content: Published Version
  • Language: English
  • Description: Nationallizenz 142-005