The general stochastic optimal control (SOC) problem in robotics scenarios is often too complex to be solved exactly and in near real time. A classical approximate solution is to ...
In this paper we develop a probabilistic interpretation and a full Bayesian inference for non-negative matrix deconvolution (NMFD) model. Our ultimate goal is unsupervised extract...
We present the Vida family of abstractions of anonymous communication systems, model them probabilistically and apply Bayesian inference to extract patterns of communications and u...
— When a network of robots or static sensors is emplaced in an environment, the spatial relationships between the sensing units must be inferred or computed for most key applicat...
Abstract. Mobile devices get to handle much information thanks to the convergence of diverse functionalities. Their environment has great potential of supporting customized service...