A communication network can be modelled as a probabilistic graph where each of b edges represents a communication line and each of n vertices represents a communication processor. Each edge e (vertexâ€¦ (More)

This work examines important issues in probabilistic temporal representation and reasoning using Bayesian networks (also known as belief networks). The representation proposed here utilizes temporalâ€¦ (More)

1. INTRODUCTION We follow graph theoretic terminology as in [B&M]. Let G = (V 9 E) denote a graph where V is a set of vertices and E is a set of nonoriented edges. Though we do not in generalâ€¦ (More)