Background: Mocapy++ is a toolkit for parameter learning and inference in dynamic Bayesian networks (DBNs). It supports a wide range of DBN architectures and probability distribut...
Many complex, real world phenomena are difficult to study directly using controlled experiments. Instead, the use of computer simulations has become commonplace as a feasible alte...
The standard framework of machine learning problems assumes that the available data is independent and identically distributed (i.i.d.). However, in some applications such as image...
Model checking has proven to be a useful analysis technique not only for concurrent systems, but also for the genetic regulatory networks (Grns) that govern the functioning of livi...
Radu Mateescu, Pedro T. Monteiro, Estelle Dumas, H...
This paper presents an efficient solution technique for the steady-state analysis of the second-order Stochastic Fluid Model underlying a second-order Fluid Stochastic Petri Net (...