Inference is a key component in learning probabilistic models from partially observable data. When learning temporal models, each of the many inference phases requires a complete ...
Passage time densities are useful performance measurements in stochastic systems. With them the modeller can extract probabilistic quality-of-service guarantees such as: the proba...
Jeremy T. Bradley, Stephen T. Gilmore, Nigel Thoma...
Abstract. Given the substantial computational requirements of stochastic simulation, approximation is essential for efficient analysis of any realistic biochemical system. This pap...
Continuous-time Bayesian networks is a natural structured representation language for multicomponent stochastic processes that evolve continuously over time. Despite the compact r...
Relational world models that can be learned from experience in stochastic domains have received significant attention recently. However, efficient planning using these models rema...