Cart (Loading....) | Create Account
Close category search window
 

Radial basis function regression using trans-dimensional sequential Monte Carlo

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
$31 $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

3 Author(s)

We consider the general problem of sampling from a sequence of distributions that is defined on a union of sub-spaces. We will illustrate the general approach on the problem of sequential radial basis function (RBF) regression where the number of kernels is variable and unknown. Our approach, which we term trans-dimensional sequential Monte Carlo (TD-SMC), is based on a generalisation of importance sampling to spaces of variable dimension. In the spirit of P. Del Moral and A. Doucet (2002) we augment the target parameter space at the current time step with an auxiliary space corresponding to the parameters at the previous time step. This facilitates the design of efficient proposal distributions, which can then be formulated as moves from the auxiliary parameter space to the target parameter space, lending our algorithm its sequential character. These proposals are very general, and may include within model moves to update parameters, and trans-dimensional birth or death moves to add or remove parameters when appropriate. From this perspective our approach is reminiscent of the reversible jump Markov Chain Monte Carlo (RJ-MCMC) algorithm [P.J. Green, 1995].

Published in:

Statistical Signal Processing, 2003 IEEE Workshop on

Date of Conference:

28 Sept.-1 Oct. 2003

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.