A Chaotic Probability model is a usual set of probability measures, M, the totality of which is endowed with an objective, frequentist interpretation as opposed to being viewed as...
Nonparametric methods are widely applicable to statistical learning problems, since they rely on a few modeling assumptions. In this context, the fresh look advocated here permeat...
Background: The incorporation of statistical models that account for experimental variability provides a necessary framework for the interpretation of microarray data. A robust ex...
Kevin A. Greer, Matthew R. McReynolds, Heddwen L. ...
—We present an alternative analysis of weighted 1 minimization for sparse signals with a nonuniform sparsity model, and extend our results to nuclear norm minimization for matric...
Stemming algorithms find canonical forms for inflected words, e. g. for declined nouns or conjugated verbs. Since such a unification of words with respect to gender, number, time, ...