Hidden Markov models (HMM) describe how an unobserved phenomenon changes over time; e.g. the behaviour of an animal (from telemetry data), or an animal's availability (from line transect data), or an animal's survival (from capture-recapture data).
In the mural, the yellow circles represent the observed data at time points t-1, t, t+1 and t+2, while the light and dark red circles represent the unobserved states at these time points. The white arrows indicate the dependencies between them.
Borchers, D.L., Zucchini, W., Heide-Jørgensen, M.P., Cañadas, A. and Langrock, R., 2013. Using hidden Markov models to deal with availability bias on line transect surveys. Biometrics, 69(3), pp.703-713.
Langrock, R., King, R., Matthiopoulos, J., Thomas, L., Fortin, D. and Morales, J.M., 2012. Flexible and practical modeling of animal telemetry data: hidden Markov models and extensions. Ecology, 93(11), pp.2336-2342.