Stochastic modeling of aphid population growth with nonlinear, power-law dynamics

DOI10.1016/j.mbs.2006.11.004zbMath1119.92052OpenAlexW1989801592WikidataQ51922109 ScholiaQ51922109MaRDI QIDQ2382396

James H. Matis, Timothy I. Matis, Douglass E. Stevenson, Thomas R. Kiffe

Publication date: 9 October 2007

Published in: Mathematical Biosciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.mbs.2006.11.004

zbMATH Keywords

Moment closure methodsNormal approximationBirth-death processesKolmogrov equationsPecan aphids

Mathematics Subject Classification ID

Signal detection and filtering (aspects of stochastic processes) (60G35) Applications of branching processes (60J85) Population dynamics (general) (92D25) Qualitative investigation and simulation of ordinary differential equation models (34C60)

Related Items (8)

The topology of scale-free networks with an S-shaped nonlinear growth characteristic ⋮ Analysis of variance of integro-differential equations with application to population dynamics of cotton aphids ⋮ Analysis of forest thinning strategies through the development of space-time growth-interaction simulation models ⋮ Generalized aphid population growth models with immigration and cumulative-size dependent dynamics ⋮ Scalable inference for Markov processes with intractable likelihoods ⋮ Bayesian design of experiments for intractable likelihood models using coupled auxiliary models and multivariate emulation ⋮ Efficient construction of Bayes optimal designs for stochastic process models ⋮ Efficient sampling of conditioned Markov jump processes

Uses Software

• EasyFit

Cites Work

• On stochastic logistic population growth models with immigration and multiple births
• On the cumulants of population size for the stochastic power law logistic model
• Effects of immigration on some stochastic logistic models: A cumulant truncation analysis
• Nonlinear stochastic modeling of aphid population growth
• Modelling Biological Populations in Space and Time
• Applying the saddlepoint approximation to bivariate stochastic processes
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