The dynamic hierarchical Dirichlet process (dHDP) is developed to model the timeevolving statistical properties of sequential data sets. The data collected at any time point are r...
Motivated by the Markov chain Monte Carlo (MCMC) relaxation method of Jalali and Weissman, we propose a lossy compression algorithm for continuous amplitude sources that relies on...
— We define the concept of approximate domain optimizer for deterministic and expected value optimization criteria. Roughly speaking, a candidate optimizer is an approximate dom...
Andrea Lecchini-Visintini, John Lygeros, Jan M. Ma...
In this paper, we present a new approach based on Markov Chain Monte Carlo(MCMC) for the stable monocular tracking of variable interacting targets in 3D space. The crucial problem...
In this paper, we present a variational Bayesian (VB) approach to computing the interval estimates for nonhomogeneous Poisson process (NHPP) software reliability models. This appr...
Hiroyuki Okamura, Michael Grottke, Tadashi Dohi, K...