Traditional Markov network structure learning algorithms perform a search for globally useful features. However, these algorithms are often slow and prone to finding local optima d...
This paper reports on experimental results with symbolic model checking of probabilistic processes based on Multi-Terminal Binary Decision Diagrams (MTBDDs). We consider concurrent...
Luca de Alfaro, Marta Z. Kwiatkowska, Gethin Norma...
Abstract. Across a wide range of domains, there is an urgent need for a wellfounded approach to incorporating uncertain and incomplete knowledge into formal domain ontologies. Alth...
This paper considers time-varying uncertain constrained systems, and develops a method for computing a probabilistic output admissible (POA) set. This set consists of the initial ...
A generative probabilistic model for objects in images is presented. An object consists of a constellation of features. Feature appearance and pose are modeled probabilistically. ...