This paper focuses on the distributed static estimation problem and a Belief Propagation (BP) based estimation algorithm is proposed. We provide a complete analysis for convergence and accuracy of it. More precisely, we ...

In a complex system, perturbations propagate by following paths on the network of interactions among the system's units. In contrast to what happens with the spreading of epidemics, observations of general perturbations ...

Credal networks generalize Bayesian networks relaxing numerical parameters. This considerably expands expressivity. but makes belief updating a hard task even on polytrees. Nevertheless, if all the variables are binary, ...

El estudio de la confianza entre los usuarios de plataformas virtuales o de redes sociales en los últimos años ha ganado interés, ya que ayuda a dar fiabilidad en la información que se brinda en dichos medios. Una ...

This paper presents a family of algorithms for approximate inference in credal networks (that is, models based on directed acyclic graphs and set-valued probabilities) that contain only binary variables. Such networks can ...

Deep learning techniques usually face drawbacks related to the vanishing gradient problem, i.e., the gradient becomes gradually weaker when propagating from one layer to another until it finally vanishes away and no longer ...

The Deep Learning models have drawn ever-increasing research interest owing to their intrinsic capability of overcoming the drawback of traditional algorithm. Hence, we have adopted the representative Deep Learning methods ...