Gene network reconstruction is a multidisciplinary research area involving data mining, machine learning, statistics, ontologies and others. Reconstructed gene network allows us t...
This paper presents a new deterministic approximation technique in Bayesian networks. This method, "Expectation Propagation," unifies two previous techniques: assumed-de...
Object detection using Haar-like features is formulated as a maximum likelihood estimation. Object features are described by an arbitrary Bayesian Network (BN) of Haar-like featur...
In this paper, we present SURCH, a novel decentralized algorithm for efficient processing of queries generated in sensor networks. Unlike existing techniques, SURCH is fully dist...
A bayesian network is an appropriate tool for working with uncertainty and probability, that are typical of real-life applications. In literature we find different approaches for b...
Evelina Lamma, Fabrizio Riguzzi, Andrea Stambazzi,...