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 language | English (US) |
|---|---|
| Title of host publication | Handbook of Statistics |
| Editors | Arni S.R. Srinivasa Rao, C.R. Rao |
| Publisher | Elsevier B.V. |
| Pages | 443-480 |
| Number of pages | 38 |
| ISBN (Print) | 9780444640727 |
| DOIs | |
| State | Published - Jan 1 2018 |
Publication series
| Name | Handbook of Statistics |
|---|---|
| Volume | 39 |
| ISSN (Print) | 0169-7161 |
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All Science Journal Classification (ASJC) codes
- Statistics and Probability
- Modeling and Simulation
- Applied Mathematics
Cite this
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Stochastic Population Models. / Fricks, John; Hanks, Ephraim Mont.
Handbook of Statistics. ed. / Arni S.R. Srinivasa Rao; C.R. Rao. Elsevier B.V., 2018. p. 443-480 (Handbook of Statistics; Vol. 39).Research output: Chapter in Book/Report/Conference proceeding › Chapter
TY - CHAP
T1 - Stochastic Population Models
AU - Fricks, John
AU - Hanks, Ephraim Mont
PY - 2018/1/1
Y1 - 2018/1/1
N2 - 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.
AB - 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.
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UR - http://www.scopus.com/inward/citedby.url?scp=85052972832&partnerID=8YFLogxK
U2 - 10.1016/bs.host.2018.07.012
DO - 10.1016/bs.host.2018.07.012
M3 - Chapter
AN - SCOPUS:85052972832
SN - 9780444640727
T3 - Handbook of Statistics
SP - 443
EP - 480
BT - Handbook of Statistics
A2 - Srinivasa Rao, Arni S.R.
A2 - Rao, C.R.
PB - Elsevier B.V.
ER -