Posterior sampling with improved efficiency

Conference · Tue Dec 01 04:00:00 EST 1998

OSTI ID:677138

Hanson, K M; Cunningham, G S

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.

• Research Organization: Los Alamos National Lab., NM (United States)
• Sponsoring Organization: USDOE, Washington, DC (United States)
• DOE Contract Number: W-7405-ENG-36
• OSTI ID: 677138
• Report Number(s): LA-UR--98-1518; CONF-980266--; ON: DE99000696
• Country of Publication: United States
• Language: English

Related Subjects

99 GENERAL AND MISCELLANEOUS
ALGORITHMS
DATA ANALYSIS
DATA COVARIANCES
IMAGE PROCESSING
MARKOV PROCESS
MATRICES
MONTE CARLO METHOD
OPTIMIZATION