The design of complex artificial populations is the first step in simulating evolution during the time span of socioeconomic variables as the family income. In this paper, a new h...
: In this work we introduce an iterative method that deforms brain models built from tomographic images. The deformation is used for normalization purposes: individual models are d...
This paper describes a framework for learning probabilistic models of objects and scenes and for exploiting these models for tracking complex, deformable, or articulated objects i...
We use a generative history-based model to predict the most likely derivation of a dependency parse. Our probabilistic model is based on Incremental Sigmoid Belief Networks, a rec...
Model-checkers are powerful tools that can find individual traces through models to satisfy desired properties. These traces provide solutions to a number of problems. Instead of...