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)
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