…but also opportunities! Hidden Markov models (HMMs) and their extensions are attractive methods for analysing ecological data. In recent years, a variety of extensions of the basic model have been proposed, yielding great opportunities for ecological inference. Yet, as these models become more complex and challenging to understand, it is important to consider what pitfalls these methods have and what opportunities there are for future research to address these pitfalls.
Glennie et al. review five pitfalls one can encounter when using HMMs or their extensions to solve ecological problems. Their aim is to heighten awareness of the pitfalls ecologists may encounter when applying these more advanced methods, but also, by highlighting future research opportunities, to inspire ecological statisticians to weaken these pitfalls and provide improved methods. The following infographic illustrates the five pitfalls discussed in the paper:
To find out more, read the full article here: Hidden Markov models: pitfalls and opportunities in ecology
This post was provided by author Timo Adam, a research fellow in statistics at the University of St Andrews.