Bayesian networks provide a modeling language and associated inference algorithm for stochastic domains. They have been successfully applied in a variety of medium-scale applicati...
With large amounts of correlated probabilistic data being generated in a wide range of application domains including sensor networks, information extraction, event detection etc.,...
This paper deduces a new model aimed at simulating injection molding processes under isothermal conditions. These processes can be generally stated as infiltration problems in init...
Episodic knowledge is often stored in the form of textual narratives written in natural language. However, a large repository of such narratives will contain both repetitive and n...
– Probabilistic Inference Networks are becoming increasingly popular for modeling and reasoning in uncertain domains. In the past few years, many efforts have been made in learni...