Challenges and Development of Hidden Markov Models in Behavioral Analysis

The chapter explores the drawbacks and complexities of hidden Markov models in analyzing behavior, highlighting issues like determining optimal states and computational costs. It discusses the development of R packages like Move HMM, Momentum, and HMM TMB for movement data analysis, catering to ecologists with varying technical backgrounds. The potential applications of deep learning alongside HMMs in understanding animal behavior and the interpretability challenges of hidden Markov models in ecology are also examined.