Gaussian process classifiers (GPCs) are Bayesian probabilistic kernel classifiers. In GPCs, the probability of belonging to a certain class at an input location is monotonically re...
The ratio of the largest eigenvalue divided by the trace of a p×p random Wishart matrix with n degrees of freedom and identity covariance matrix plays an important role in variou...
We propose a general framework to index very large datasets of spatial data in a distributed system. Our proposal is built on the recently proposed Scalable Distributed Rtree (SD-...
Quantifier reasoning in Satisfiability Modulo Theories (SMT) is a long-standing challenge. The practical method employed in modern SMT solvers is to instantiate quantified formulas...
Probability distributions are useful for expressing the meanings of probabilistic languages, which support formal modeling of and reasoning about uncertainty. Probability distribu...