Particle filters (PFs) are powerful samplingbased inference/learning algorithms for dynamic Bayesian networks (DBNs). They allow us to treat, in a principled way, any type of prob...
Arnaud Doucet, Nando de Freitas, Kevin P. Murphy, ...
This paper introduces some preliminary results of the standardization process of ITU-T's H.26L project. This forthcoming video coding standard will not only significantly imp...
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...
In [Dague, 1993], a formal system ROM(K) involving four relations has been defined to reason with relative orders of magnitude. In this paper, problems of introducing quantitative...
Linguistic access to uncertain quantitative knowledge about physical properties is provided by dimensional adjectives, e.g. long-short in the spatial and temporal senses, near-far...