An article published in Movement Ecology discusses how the versatile flexible hidden Markov models can be used to jointly learn about animal behaviour, habitat selection and space use in a wide range of species and systems.
$$\begin{aligned} \tilde{p}=\frac{\exp \{\varvec{c} \cdot \varvec{\beta }^{}\}}{\frac{1}{N+1} \sum _{i=0}^N\exp \{\varvec{c} \cdot \varvec{\beta }^{} \}} \end{aligned}$$in the denominator is often omitted when it is constant; here, we include it explicitly as the number of valid control locations may vary between steps due to missing covariate data. In general, the choice ofthe approximation approaches the true likelihood.
The method to generate control locations in an SSF is an important choice, with potential implications on the precision of the estimation. Equation. In practice, it is more common to simulate control locations on a disc that is sufficiently large to encompass the vast majority of the probability mass, as increasing radius size much beyond the maximum observed step does little to improve the estimation [].
$$\begin{aligned} \tilde{p}=\frac{\exp \{\varvec{c} \cdot \varvec{\beta }^{}\}}{\frac{1}{N+1} \sum _{i=0}^N \exp \{\varvec{c} \cdot \varvec{\beta }^{} \} / h} \end{aligned}$$, to take advantage of the animal’s tendency to favour short steps, and
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