Xinxiu Wang

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In nature, population dynamics are subject to multiple sources of stochasticity. State-space models (SSMs) provide an ideal framework for incorporating both environmental noises and measurement errors into dynamic population models. In this paper, we present a recently developed method, Particle Markov Chain Monte Carlo (Particle MCMC), for parameter(More)
Spatial aggregation and self-similarity are two important properties in species spatial distribution analysis and modeling. The aggregation parameter k in the negative binomial distribution model and fractal dimension are two widely used measures of spatial aggregation and self-similarity, respectively. In this paper, we attempt to describe spatial(More)
Stochastic dynamical systems have been increasingly used in natural sciences. Data assimilation, which can effectively combine observation data and theoretical models, improves the applicability of dynamical models. In this study, a statistical data assimilation method, Bayesian filtering, is presented. Its performance is examined with a dynamical model of(More)
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