  We compare dynamic mean-field and dynamic cavity as methods to describe the
stationary states of dilute kinetic Ising models. We compute dynamic mean-field
theory by expanding in interaction strength to third order, and compare to the
exact dynamic mean-field theory for fully asymmetric networks. We show that in
diluted networks the dynamic cavity method generally predicts magnetizations of
individual spins better than both first order ("naive") and second order
("TAP") dynamic mean field theory.

Disordered Systems and Neural Networks;Statistical Mechanics