Inference is a key component in learning probabilistic models from partially observable data. When learning temporal models, each of the many inference phases requires a complete ...
Identification and characterization of indications in eddy current (ET) signals can be highly subjective in nature, with varying diagnoses made by different analysts or by a singl...
Arthur J. Levy, Jane E. Oppenlander, David M. Brud...
In learning belief networks, the single link lookahead search is widely adopted to reduce the search space. We show that there exists a class of probabilistic domain models which ...
In evolutionary algorithms, the typical post-processing phase involves selection of the best-of-run individual, which becomes the final outcome of the evolutionary run. Trivial f...
In this paper we present a novel probabilistic protocol for path discovery in Mobile Ad Hoc Networks (MANETs). The protocol implements what we call a polarized gossiping algorithm...