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...
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,...
In this work, we embark on a study of the possibility (or impossibility), and the corresponding costs, of devising concurrent, low-contention implementations of atomic Read-Modify...
Costas Busch, Marios Mavronicolas, Paul G. Spiraki...
In this paper we present an algorithm and software for generating arbitrarily large Bayesian Networks by tiling smaller real-world known networks. The algorithm preserves the stru...
Ioannis Tsamardinos, Alexander R. Statnikov, Laura...
One of the most important fundamental properties of Bayesian networks is the representational power, reflecting what kind of functions they can or cannot represent. In this paper,...