State Space and Hidden Markov Models
This chapter surveys state space and hidden Markov modelling approaches for analyzing time series or longitudinal data, spatial data, and spatiotemporal data. Responses are generally non-Gaussian, in particular, categorical, counted or nonnegative. State space and hidden Markov models have the common feature that they relate responses to unobserved “states” or “parameters” by an observation model. The states, which may represent, e.g., an unobserved temporal or spatial trend or time-varying covariate effects, are assumed to follow a latent or “hidden” Markov model.
KeywordsHide Markov Model Kalman Filter State Space Model Markov Random Field Observation Model
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