By Topic

A Bayesian approach for the estimation of model parameters from noisy data sets

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
S. J. Payne ; Dept. of Eng. Sci., Univ. of Oxford, UK

A Bayesian method is proposed for estimating model parameters from noisy data sets. The method is based on maximizing the posterior kernel, which enables priors on the model parameters to be incorporated. The posterior kernel is found by specifying hyperpriors and integrating the priors out, due to the use of conjugate priors. The use of probability models enables simultaneous data streams to be used to maximize the posterior kernel. The solution is found using an iterative scheme. The algorithm's performance is briefly illustrated using a real data set, demonstrating rapid convergence.

Published in:

IEEE Signal Processing Letters  (Volume:12 ,  Issue: 8 )