  The biased net paradigm was the first general and empirically tractable
scheme for parameterizing complex patterns of dependence in networks,
expressing deviations from uniform random graph structure in terms of latent
``bias events,'' whose realizations enhance reciprocity, transitivity, or other
structural features. Subsequent developments have introduced local
specifications of biased nets, which reduce the need for approximations
required in early specifications based on tracing processes. Here, we show that
while one such specification leads to inconsistencies, a closely related
Markovian specification both evades these difficulties and can be extended to
incorporate new types of effects. We introduce the notion of inhibitory bias
events, with satiation as an example, which are useful for avoiding
degeneracies that can arise from closure bias terms. Although our approach does
not lead to a computable likelihood, we provide a strategy for approximate
Bayesian inference using random forest prevision. We demonstrate our approach
on a network of friendship ties among college students, recapitulating a
relationship between the sibling bias and tie strength posited in earlier work
by Fararo.
