Abstract

this article, we show that this linkage of attractor dynamics and probabilistic inference is not limited to symmetric networks or (equivalently) to models represented as undirected graphs. We investigate an attractor dynamics for feedforward networks, or directed acyclic graphs (DAGs); these are networks with directed edges but no directed loops. The probabilistic models represented by DAGs are known as Bayesian networks, and together with MRFs, they comprise the class of probabilistic models known as graphical models (Lauritzen, 1996). Like their undirected counterparts, Bayesian networks have been proposed as models of both artificial and biological intelligence (Pearl, 1988).

Citations