  Learning the parameters of a (potentially partially observable) random field
model is intractable in general. Instead of focussing on a single optimal
parameter value we propose to treat parameters as dynamical quantities. We
introduce an algorithm to generate complex dynamics for parameters and (both
visible and hidden) state vectors. We show that under certain conditions
averages computed over trajectories of the proposed dynamical system converge
to averages computed over the data. Our "herding dynamics" does not require
expensive operations such as exponentiation and is fully deterministic.
