We consider probabilistic inference in general hybrid networks, which include continuous and discrete variables in an arbitrary topology. We reexamine the question of variable dis...
We consider the problem of learning Gaussian multiresolution (MR) models in which data are only available at the finest scale and the coarser, hidden variables serve both to captu...
Myung Jin Choi, Venkat Chandrasekaran, Alan S. Wil...
Following Breiman’s methodology, we propose a multi-classifier based on a “forest” of randomly generated fuzzy decision trees, i.e., a Fuzzy Random Forest. This approach co...
Background: Large databases of genetic data are often biased in their representation. Thus, selection of genetic data with desired properties, such as evolutionary representation ...
Mohan Krishnamoorthy, Pragneshkumar Patel, Mira Di...
Point estimates of the parameters in real world models convey valuable information about the actual system. However, parameter comparisons and/or statistical inference requires de...