  We propose a Bayesian stochastic cellular automata modeling approach to model
the spread of wildfires with uncertainty quantification. The model considers a
dynamic neighborhood structure that allows neighbor states to inform transition
probabilities in a multistate categorical model. Additional spatial information
is captured by the use of a temporally evolving latent spatio-temporal dynamic
process linked to the original spatial domain by spatial basis functions. The
Bayesian construction allows for uncertainty quantification associated with
each of the predicted fire states. The approach is applied to a heavily
instrumented controlled burn.
