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Posterior sampling with improved efficiency [electronic resource].

Published
Washington, D.C. : United States. Dept. of Energy, 1998.
Oak Ridge, Tenn. : Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy.
Physical Description
12 pages : digital, PDF file
Additional Creators
Los Alamos National Laboratory, United States. Department of Energy, and United States. Department of Energy. Office of Scientific and Technical Information
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Summary
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.
Report Numbers
E 1.99:la-ur--98-1518
E 1.99: conf-980266--
conf-980266--
la-ur--98-1518
Subject(s)
Other Subject(s)
Note
Published through SciTech Connect.
12/01/1998.
"la-ur--98-1518"
" conf-980266--"
"DE99000696"
Physics of medical imaging, San Diego, CA (United States), 22-26 Feb 1998.
Cunningham, G.S.; Hanson, K.M.
Funding Information
W-7405-ENG-36
View MARC record | catkey: 14351583