  We study normal approximations for a class of discrete-time occupancy
processes, namely, Markov chains with transition kernels of product Bernoulli
form. This class encompasses numerous models which appear in the complex
networks literature, including stochastic patch occupancy models in ecology,
network models in epidemiology, and a variety of dynamic random graph models.
Bounds on the rate of convergence for a central limit theorem are obtained
using Stein's method and moment inequalities on the deviation from an analogous
deterministic model. As a consequence, our work also implies a uniform law of
large numbers for a subclass of these processes.
