— In this paper, we present an approach allowing a robot to learn a generative model of its own physical body from scratch using self-perception with a single monocular camera. O...
We present node level primitives for parallel exact inference on an arbitrary Bayesian network. We explore the probability representation on each node of Bayesian networks and eac...
Recent work in applying causal modeling (Bayesian networks) to software engineering has resulted in improved decision support systems for software project managers. Once the causa...
Peter Hearty, Norman E. Fenton, Martin Neil, Patri...
The problem of distributed Bayesian estimation is considered in the context of a wireless sensor network. The Bayesian estimation performance is analyzed in terms of the expected F...
—Mining temporal network models from discrete event streams is an important problem with applications in computational neuroscience, physical plant diagnostics, and human-compute...