State Space and Hidden Markov Models

  • Ludwig Fahrmeir
  • Gerhard Tutz
Part of the Springer Series in Statistics book series (SSS)


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.


Hide Markov Model Kalman Filter State Space Model Markov Random Field Observation Model 
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Copyright information

© Springer Science+Business Media New York 2001

Authors and Affiliations

  • Ludwig Fahrmeir
    • 1
  • Gerhard Tutz
    • 2
  1. 1.Department of StatisticsUniversity of MunichMünchenGermany
  2. 2.Department of StatisticsUniversity of MunichMünchenGermany

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