4 papers · 1 filter
Maximum approximate likelihood estimation of general continuous-time state-space models
Sina Mews, Roland Langrock, Marius Ötting +2
Continuous-time state-space models (SSMs) are flexible tools for analysing irregularly sampled sequential observations that are driven by an underlying state process. Corresponding…
A primer on coupled state-switching models for multiple interacting time series
Jennifer Pohle, Roland Langrock, Mihaela van der Schaar +2
State-switching models such as hidden Markov models or Markov-switching regression models are routinely applied to analyse sequences of observations that are driven by underlying n…
Penalized estimation of flexible hidden Markov models for time series of counts
Timo Adam, Roland Langrock, Christian H. Weiß
Hidden Markov models are versatile tools for modeling sequential observations, where it is assumed that a hidden state process selects which of finitely many distributions generate…
Multi-scale modeling of animal movement and general behavior data using hidden Markov models with hierarchical structures
Vianey Leos-Barajas, Eric Gangloff, Timo Adam +4
Hidden Markov models (HMMs) are commonly used to model animal movement data and infer aspects of animal behavior. An HMM assumes that each data point from a time series of observat…