We present an algorithm for fast posterior inference in penalized high-dimensional state-space models, suitable in the case where a few measurements are taken in each time step. W...
Advances in networking technology have enabled network engineers to use sampled data from routers to estimate network flow volumes and track them over time. However, low sampling ...
In this work we extend results from the literature on H design with pole placement constraints to the case of generalized state space models, for both continuous-time and discrete...
Analytical and simulative modeling for dependability and performance evaluation has been proven to be a useful and versatile approach in all the phases of the system life cycle. I...
Stefano Porcarelli, Felicita Di Giandomenico, Paol...
A family of probabilistic time series models is developed to analyze the time evolution of topics in large document collections. The approach is to use state space models on the n...