Statistical analysis of an ecological competition model - Nsengiyumva, Cyprien
26,99 €
inkl. MwSt.
Versandkostenfrei*
Versandfertig in 6-10 Tagen
  • Broschiertes Buch

This work focused on statistical analysis of an ecological competition model. Literature based on bayesian inference in parameter estimation and on Markov Chain Monte Carlo (MCMC)have been developed. The model is a system of differential equations expressing the behavior of two populations competing for the same living resources.Unknown parameters were estimated by least square methods.Finally MCMC approach have been used to produce chains of samples from the posterior distribution over parameters. By using this model enough evolution of population in competition was predicted.All the results…mehr

Andere Kunden interessierten sich auch für
Produktbeschreibung
This work focused on statistical analysis of an ecological competition model. Literature based on bayesian inference in parameter estimation and on Markov Chain Monte Carlo (MCMC)have been developed. The model is a system of differential equations expressing the behavior of two populations competing for the same living resources.Unknown parameters were estimated by least square methods.Finally MCMC approach have been used to produce chains of samples from the posterior distribution over parameters. By using this model enough evolution of population in competition was predicted.All the results obtained from analytical solution and either from least square estimation and MCMC analysis can conduct to assume that the creation and the management of the system where two or more populations can coexist and compete for the same living resources is possible.
Autorenporträt
Was born in Rwanda. He holds a bachelor's degree in Applied Mathematics obtained from National University of Rwanda in 2008 and a master's degree in Technomathematics obtained from Lappeenranta University of Technology, Finland in 2011. He is now teaching Mathematics at Catholic University of Rwanda located in Huye District, Southern Province.