Hidden Markov models for individual time series

Ingmar Visser

pp. 269-289

in: Jaan Valsiner, Peter Molenaar, Maria Lyra, Nandita Chaudhary (eds), Dynamic process methodology in the social and developmental sciences, Berlin, Springer, 2009

Abstract

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.