Journal
NEUROCOMPUTING
Volume 72, Issue 1-3, Pages 32-38Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.neucom.2007.12.046
Keywords
Delay estimation; Irregular sampling; Bayesian methods; MCMC; Kalman filtering
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A method for estimating time delays between signals that are irregularly sampled is presented. The approach is based on postulating a latent variable model encoding the assumption of slow variability of the underlying source signal. The posterior distribution of the delay is obtained partly by exact marginalisation computable by a specific type of Kalman filter and partly by Markov chain Monte Carlo. Experiments with artificial data show the effectiveness of the proposed approach while results with real-world gravitational lens data provide the main motivation for this work. (C) 2008 Elsevier B.V. All rights reserved.
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