Stochastic Population Models

John Fricks, Ephraim Hanks

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

In this chapter, we introduce stochastic population processes, and more specifically Markov population processes. We give basic definitions and examples from the scientific literature to illustrate the process of building these stochastic models. We then discuss approximations to these stochastic processes when the population is large and review numerical schemes for stochastic simulation that rely on these approximations. We then review and suggest practical statistical inference methods for observations that arise from these stochastic population models, including when these models are generalized to a spatio-temporal framework.

Original languageEnglish (US)
Title of host publicationHandbook of Statistics
EditorsArni S.R. Srinivasa Rao, C.R. Rao
PublisherElsevier B.V.
Pages443-480
Number of pages38
ISBN (Print)9780444640727
DOIs
StatePublished - Jan 1 2018

Publication series

NameHandbook of Statistics
Volume39
ISSN (Print)0169-7161

Keywords

  • Diffusion approximation
  • Markov processes
  • Spatio-temporal statistics
  • Statistical inference for stochastic processes
  • Stochastic population models
  • Stochastic simulation

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation
  • Applied Mathematics

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  • Cite this

    Fricks, J., & Hanks, E. (2018). Stochastic Population Models. In A. S. R. Srinivasa Rao, & C. R. Rao (Eds.), Handbook of Statistics (pp. 443-480). (Handbook of Statistics; Vol. 39). Elsevier B.V.. https://doi.org/10.1016/bs.host.2018.07.012