This paper presents a new approach to understand the event stream model. Additionally a new approximation algorithm for the feasibility test of the sporadic and the generalized mu...
We describe a slightly subexponential time algorithm for learning parity functions in the presence of random classification noise, a problem closely related to several cryptograph...
We consider Bayesian analysis of data from multivariate linear regression models whose errors have a distribution that is a scale mixture of normals. Such models are used to analy...
—We propose a general synchronous model of lattice random fields which could be used similarly to Gibbs distributions in a Bayesian framework for image analysis, leading to algor...
A state/event model is a concurrent version of Mealy machines used for describing embedded reactive systems. This paper introduces a technique that uses compositionality and depend...