Probabilistic inference problems arise naturally in distributed systems such as sensor networks and teams of mobile robots. Inference algorithms that use message passing are a nat...
Recent theoretical work has identified random projection as a promising dimensionality reduction technique for learning mixtures of Gaussians. Here we summarize these results and ...
We examine two types of similarity networks each based on a distinct notion of relevance. For both types of similarity networks we present an efficient inference algorithm that wo...
: Jeffrey’s rule of conditioning has been proposed in order to revise a probability measure by another probability function. We generalize it within the framework of the models b...
: We construct the belief function that quantifies the agent' beliefs about which event of will occurred when he knows that the event is selected by a chance set-up and that ...