Dynamic models

This chapter covers dynamic models, an important kind of multi-level model. It shows how to simulate dynamic models, discusses process and observation error, and illustrates methods for fitting models that assume only one or the other. For problems where we want to estimate process error when the magnitude of observation error is known, it introduces the SIMEX approach. Finally, it presents a brief introduction to fitting state-space models, which can estimate both process and observation error, via the Kalman filter or Markov chain Monte Carlo.

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