Bayesian statistics is an approach to data analysis based on Bayes' theorem, where
available knowledge about parameters in a statistical model is updated with the information …

Memory is critical to understanding animal movement but has proven challenging to study.
Advances in animal tracking technology, theoretical movement models and cognitive …

Movement ecology has developed rapidly over the past decade, driven by advances in
tracking technology that have largely removed data limitations. Development of rigorous …

Discrete‐time hidden Markov models (HMMs) have become an immensely popular tool for
inferring latent animal behaviours from telemetry data. While movement HMMs typically rely …

The study of animal movement has always been a key element in ecological science,
because it is inherently linked to critical processes that scale from individuals to populations …

Ecological systems can often be characterised by changes among a finite set of underlying
states pertaining to individuals, populations, communities or entire ecosystems through time …

State–space models (SSMs) are an important modeling framework for analyzing ecological
time series. These hierarchical models are commonly used to model population dynamics …

Reveals How HMMs Can Be Used as General-Purpose Time Series ModelsImplements all
methods in RHidden Markov Models for Time Series: An Introduction Using R applies …

Abstract Hidden Markov models (HMMs) and their extensions are attractive methods for
analysing ecological data where noisy, multivariate measurements are made of a hidden …

We discuss hidden Markov‐type models for fitting a variety of multistate random walks to
wildlife movement data. Discrete‐time hidden Markov models (HMMs) achieve considerable …