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When count data arise from mixtures of unobserved populations, models based on standard probability distributions are often inadequate to explain observed variability (Frühwirth-Schnatter 2005; Wedel and DeSarbo 1995).
The framework is based on the theory of branching processes [41], [43] and age-structured populations models [40].
Canonical seed predictions have the advantage that only primary sequence information is used, so populations models (see Results) can be easily fitted.
These 3 nonspecific models differ in whether bottom-up processing activates different neuronal populations for words and pictures (Models B1 and B2) or the same neuronal populations (Models B3).
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Furthermore, Turchin and Taylor in [27] proposed the following general delayed discrete populations model: (1.1).
Population models are traditionally formulated as population balance equations.
Thus, nonlinear population models offer a more realistic approach than linear population models.
For other food-limited population models, we refer to [16 19].
Therefore, lots have been done on discrete-time population models.
For simplicity we have focused on single population models.
Population models can provide a useful tool in risk assessment.
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