This paper addresses the problem of configuring wireless sensor networks (WSNs). Specifically, we seek answers to the following questions: how many sensors should be deployed, wha...
Learning graphical models with hidden variables can offer semantic insights to complex data and lead to salient structured predictors without relying on expensive, sometime unatta...
In this paper, we provide new complexity results for algorithms that learn discrete-variable Bayesian networks from data. Our results apply whenever the learning algorithm uses a ...
David Maxwell Chickering, Christopher Meek, David ...
We extend the differential approach to inference in Bayesian networks (BNs) (Darwiche, 2000) to handle specific problems that arise in the context of dynamic Bayesian networks (D...
Road networks, roads, and junctions are examples of natural language terms whose semantics can be described by affordances of their physical referents. In order to define affordanc...