On the latent state estimation of nonlinear population dynamics using Bayesian and non-Bayesian state-space models

Ecological Modelling

Volumn 200, Issue 3-4, 2007, Pages 521-528

  Wang, Guiming ^a  

^a MISSISSIPPI STATE UNIVERSITY   (United States)

Author keywords

Bayesian nonlinear state space model; Extended Kalman filter; Markov chain Monte Carlo method; Theta logistic population models; Unscented Kalman filter

Indexed keywords

ALGORITHMS; COMPUTER SIMULATION; DATA ACQUISITION; ENVIRONMENTAL ENGINEERING; ERROR ANALYSIS; KALMAN FILTERING; MONTE CARLO METHODS; NONLINEAR SYSTEMS; PARAMETER ESTIMATION; STATE SPACE METHODS;

BAYESIAN NONLINEAR STATE-SPACE MODEL; ENVIRONMENTAL SCIENCES; EXTENDED KALMAN FILTER (EKF); POPULATION DYNAMICS; THETA LOGISTIC POPULATION MODELS; UNSCENTED KALMAN FILTER (UKF);

ECOSYSTEMS;

BAYESIAN ANALYSIS; DATA ASSIMILATION; ESTIMATION METHOD; KALMAN FILTER; MONTE CARLO ANALYSIS; POPULATION DYNAMICS;

EID: 33845452219     PISSN: 03043800     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ecolmodel.2006.09.004     Document Type: Article

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