Posterior sampling with improved efficiency

Kenneth M. Hanson; Gregory S. Cunningham

doi:10.1117/12.310914

24 June 1998 Posterior sampling with improved efficiency

Kenneth M. Hanson, Gregory S. Cunningham

Proceedings Volume 3338, Medical Imaging 1998: Image Processing; (1998) https://doi.org/10.1117/12.310914
Event: Medical Imaging '98, 1998, San Diego, CA, United States

Abstract

The Markov Chain Monte Carlo (MCMC) technique provides a means to generate a random sequence of model realizations that sample the posterior probability distribution of a Bayesian analysis. That sequence may be used to make inferences about the model uncertainties that derive from measurement uncertainties. This paper presents an approach to improving the efficiency of the Metropolis approach to MCMC by incorporating an approximation to the covariance matrix of the posterior distribution. The covariance matrix is approximated using the update formula from the BFGS quasi-Newton optimization algorithm. Examples are given for uncorrelated and correlated multidimensional Gaussian posterior distributions.

Citation Download Citation

Kenneth M. Hanson and Gregory S. Cunningham "Posterior sampling with improved efficiency", Proc. SPIE 3338, Medical Imaging 1998: Image Processing, (24 June 1998); https://doi.org/10.1117/12.310914

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