Hierarchical Sampling with Constraints

2Citations
Citations of this article
5Readers
Mendeley users who have this article in their library.
Get full text

Abstract

Reconstruction of porous media images is required in order to study different properties of these material. Our research interest is on generating samples from the posterior model in which low resolution measurements are combined with a prior model. The reconstruction task becomes intractable when the size of the samples increases, since it is based on simulated annealing which is a slow convergence algorithm. The hierarchical approaches have been applied to tackle this problem, in the case of sampling from the prior model. However, in the posterior sampling case, defining a suitable measurement model at each scale still remains a challenging task. In this paper we define how we can incorporate the measurement in the hierarchical posterior model and then how we generate samples from that model. © 2009 Springer Berlin Heidelberg.

Cite

CITATION STYLE

APA

Mohebi, A., Liu, Y., & Fieguth, P. (2009). Hierarchical Sampling with Constraints. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5627 LNCS, pp. 23–32). https://doi.org/10.1007/978-3-642-02611-9_3

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free