Bayesian part tolerancing with measurement uncertainty

Alison J. Noble; Joseph L. Mundy

doi:10.1117/12.162046

29 October 1993 Bayesian part tolerancing with measurement uncertainty

Alison J. Noble, Joseph L. Mundy

Proceedings Volume 2032, Neural and Stochastic Methods in Image and Signal Processing II; (1993) https://doi.org/10.1117/12.162046
Event: SPIE's 1993 International Symposium on Optics, Imaging, and Instrumentation, 1993, San Diego, CA, United States

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.

Citation Download Citation

Alison J. Noble and Joseph L. Mundy "Bayesian part tolerancing with measurement uncertainty", Proc. SPIE 2032, Neural and Stochastic Methods in Image and Signal Processing II, (29 October 1993); https://doi.org/10.1117/12.162046

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