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Proceedings Paper

Bayesian part tolerancing with measurement uncertainty
Author(s): Alison J. Noble; Joseph L. Mundy
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Paper Abstract

Gibbs sampling, and other stochastic simulation methods, have recently received considerable attention in Bayesian statistics. Significant progress has been made in the areas of developing techniques for sampling from non-conjugate distributions, and analyzing theoretical and practical aspects relating to convergence. One of the powers of Gibbs sampling is the way it can simplify the expression of data models by replacing the evaluation of the integrals needed to compute the relevant posterior quantities by sampling from multidimensional distributions. This has opened up the way to solve complex Bayesian models that are not analytically tractable. In this paper we show how to separate variability in model parameters from variability due to the model extraction process by fitting hierarchial models to image sequences using Gibbs sampling. First, we review some of the recent developments in Gibbs sampling. Then we describe some of our experimental work using Gibbs sampling to extract geometric parameter distributions from industrial images.

Paper Details

Date Published: 29 October 1993
PDF: 12 pages
Proc. SPIE 2032, Neural and Stochastic Methods in Image and Signal Processing II, (29 October 1993); doi: 10.1117/12.162046
Show Author Affiliations
Alison J. Noble, GE Corporate Research and Development Ctr. (United Kingdom)
Joseph L. Mundy, GE Corporate Research and Development Ctr. (United States)


Published in SPIE Proceedings Vol. 2032:
Neural and Stochastic Methods in Image and Signal Processing II
Su-Shing Chen, Editor(s)

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