— Many inference problems that arise in sensor networks require the computation of a global conclusion that is consistent with local information known to each node. A large class...
Probabilistic inference in graphical models is a prevalent task in statistics and artificial intelligence. The ability to perform this inference task efficiently is critical in l...
The presence of non-symmetric evidence has been a barrier for the application of lifted inference since the evidence destroys the symmetry of the first-order probabilistic model....
Hung B. Bui, Tuyen N. Huynh, Rodrigo de Salvo Braz
Abstract. This paper introduces Higher-Order Bayesian Networks, a probabilistic reasoning formalism which combines the efficient reasoning mechanisms of Bayesian Networks with the...
Domain-specific knowledge of paintings defines a wide range of concepts for annotation and flexible retrieval of paintings. In this work, we employ the ontology of artistic concept...