Importance Sampling is a potentially powerful variance reduction technique to speed up simulations where the objective depends on the occurrence of rare events. However, it is cru...
-- Future distributed applications are expected to be deployed in an environment that is more dynamic and heterogeneous than ever before. The environment features are difficult to ...
We propose a design theory that tackles dynamic complexity in the design for Information Infrastructures (IIs) defined as a shared, open, heterogeneous and evolving socio-technica...
The ratio of two probability densities can be used for solving various machine learning tasks such as covariate shift adaptation (importance sampling), outlier detection (likeliho...
We present a theoretical framework and practical method for the automatic construction of simple, all-quadrilateral patch layouts on manifold surfaces. The resulting layouts are c...