Biological systems are traditionally studied by focusing on a specific subsystem, building an intuitive model for it, and refining the model using results from carefully designed ...
Irit Gat-Viks, Amos Tanay, Daniela Raijman, Ron Sh...
Large-scale sensor network applications require in-network processing and data fusion to compute statistically relevant summaries of the sensed measurements. This paper studies di...
Jeremy Schiff, Dominic Antonelli, Alexandros G. Di...
Tracking the identities of moving objects is an important aspect of most multi-object tracking applications. Uncertainty in sensor data, coupled with the intrinsic difficulty of ...
Jaewon Shin, Nelson Lee, Sebastian Thrun, Leonidas...
Bayesian networks (BNs) are used to represent and ef ciently compute with multi-variate probability distributions in a wide range of disciplines. One of the main approaches to per...
We investigate probabilistic propositional logic as a way of expressing and reasoning about uncertainty. In contrast to Bayesian networks, a logical approach can easily cope with i...