Hidden Markov Models (HMMs) are important tools for modeling sequence data. However, they are restricted to discrete latent states, and are largely restricted to Gaussian and disc...
Le Song, Sajid M. Siddiqi, Geoffrey J. Gordon, Ale...
In this article we analyze the combination of ACOhg, a new metaheuristic algorithm, plus partial order reduction applied to the problem of finding safety property violations in co...
This paper explores the applicability of two formal models of spatial relations, Double Cross and RfDL3-12, to interpret some typical expressions that people use for describing a r...
Hidden Markov Models (HMMs) provide a simple and effective framework for modelling time-varying spectral vector sequences. As a consequence, almost all present day large vocabula...
Abstract--We present a versatile framework for tractable computation of approximate variances in large-scale Gaussian Markov random field estimation problems. In addition to its ef...
Dmitry M. Malioutov, Jason K. Johnson, Myung Jin C...