Abstract
We present a Bayesian method that simultaneously identifies the model structure and calibrates the
parameters of a cellular automaton (CA). The method entails sequential assimilation of observations,
using a particle filter. It employs prior knowledge of experts to define which processes might be
important in the system, and uses empirical information from observations
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