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(2009) Dynamic process methodology in the social and developmental sciences, Dordrecht, Springer.
This chapter introduces hidden Markov models to study and characterize (individual) time series such as observed in psychological experiments of learning, repeated panel data, repeated observations comprising a developmental trajectory etc. Markov models form a broad and flexible class of models with many possible extensions, while at the same time allowing for relatively easy analysis and straightforward interpretation. Here we focus on hidden Markov models with a discrete underlying state space, and observations at discrete times; however, hidden Markov models are not limited to these situations and some pointers are provided to literature on possible extensions.
Publication details
DOI: 10.1007/978-0-387-95922-1_13
Full citation:
Visser, I. (2009)., Hidden Markov models for individual time series, in J. Valsiner, P. C. Molenaar, M. C. Lyra & N. Chaudhary (eds.), Dynamic process methodology in the social and developmental sciences, Dordrecht, Springer, pp. 269-289.
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