Posterior sampling with improved efficiency

Proceedings of SPIE - The International Society for Optical Engineering

Volumn 3338, Issue , 1998, Pages 371-382

  Hanson, Kenneth M ^a   Cunningham, Gregory S ^a  

^a LOS ALAMOS NATIONAL LABORATORY   (United States)

Author keywords

Adaptive Markov Chain Monte Carlo; Bayesian analysis; BFGS optimization; Statistical efficiency; Uncertainty estimation

Indexed keywords

BAYESIAN NETWORKS; COVARIANCE MATRIX; IMAGE PROCESSING; IMAGING SYSTEMS; MARKOV PROCESSES; MEDICAL IMAGING; MONTE CARLO METHODS; OPTIMIZATION; PROBABILITY DENSITY FUNCTION; PROBABILITY DISTRIBUTIONS; UNCERTAIN SYSTEMS;

ADAPTIVE MARKOV CHAIN MONTE CARLO; BAYESIAN ANALYSIS; BFGS OPTIMIZATION; STATISTICAL EFFICIENCY; UNCERTAINTY ESTIMATION;

UNCERTAINTY ANALYSIS;

EID: 0003785296     PISSN: 0277786X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1117/12.310914     Document Type: Conference Paper

References (24)

1. W. R. Gilks, S. Richardson, and D. J. Spiegelhalter, Markov Chain Monte Carlo in Practice, Chapman and Hall, London, 1996.
2. A. Gelman, J. B. Carlin, H. S. Stern, and D. B. Rubin, Bayesian Data Analysis, Chapman & Hall, London, 1995.
3. J. Besag, P. Green, D. Higdon, and K. Mengersen, "Bayesian computation and stochastic systems," Stat. Sci. 10, pp. 3-66, 1995.
4. K. M. Hanson, G. S. Cunningham, G. R. Jennings, Jr., and D. R. Wolf, "Tomographic reconstruction based on flexible geometric models," in Proc. IEEE Int. Conf. Image Processing, vol. II, pp. 145-147, IEEE, 1994.
5. K. M. Hanson, "Bayesian reconstruction based on flexible prior models," J. Opt. Soc. Amer. A 10, pp. 997-1004, 1993.
6. K. M. Hanson, G. S. Cunningham, and R. J. McKee, "Uncertainty assessment for reconstructions based on deformable models," Int. J. Imaging Systems and Technology 8, pp. 506-512, 1997.
7. G. Burgers, R. Giering, and M. Fischer, "Construction of the adjoint of the HOPE OGCM," Ann. Geophysicae. C14, p. 390, 1996.
8. G. S. Cunningham, K. M. Hanson, and X. L. Battle, "Three-dimensional reconstructions from low-count SPECT data using deformable models," Opt. Express 2, pp. 227-236, 1998.
9. K. M. Hanson, G. S. Cunningham, and S. S. Saquib, "Inversion based on computational simulations," in Maximum Entropy and Bayesian Methods, G. Erickson, ed., Kluwer Academic, Dordrecht, 1998 (to be published).
10. W. R. Gilks and G. O. Roberts, "Strategies for improving MCMC," in Markov Chain Monte Carlo in Practice, W. R. Gilks, S. Richardson, and D. J. Spiegelhalter, eds., pp. 89-114, Chapman and Hall, London, 1996.
11. A. E. Raftery and S. M. Lewis, "Implementing MCMC," in Markov Chain Monte Carlo in Practice, W. R. Gilks, S. Richardson, and D. J. Spiegelhalter, eds., pp. 115-130, Chapman and Hall, London, 1996.
12. N. Metropolis, A. W. Rosenbluth, M. N. Rosenbluth, A. H. Teller, and E. Teller, "Equations of state calculations by fast computing machine," J. Chem. Phys. 21, pp. 1087-1091, 1953.
13. M. H. Kalos, Monte Carlo Methods - Vol. 1: Basics, John Wiley and Sons, New York, 1986.
14. A. Gelman and D. B. Rubin, "Inference from iterative simulation using multiple sequences (with discussion)," Statist. Sci. 7, pp. 457-511, 1992.
15. W. K. Hastings, "Monte Carlo sampling methods using Markov chains and their applications," Biometrika 57, pp. 97-109, 1970.
16. Interactive Data Language, Research Systems, Inc., 2995 Wilderness Place, Boulder, CO 80301.
17. K. Falconer, Fractal Geometry: Mathematical Foundations and Applications, J. Wiley & Sons, New York, 1990.
18. A. Gelman, G. O. Roberts, and W. R. Gilks, "Efficient Metropolis jumping rules," in Bayesian Statistics 5, J. M. Bernardo, J. O. Berger, A. P. Dawid, and A. F. M. Smith, eds., Oxford University Press, 1996.
19. T. E. Booth, "Exponential convergence of a continous Monte Carlo transport problem," Nucl. Sci. 127, pp. 338-345, 1997.
20. R. Fletcher, "A new approach to variable metric algorithms," Computer Journal 13, pp. 317-322, 1970.
21. R. Giering, "Tangent linear and Adjoint Model Compiler," Tech. Rep. TAMC 4.7, Max-Planck-Institut für Meteorologie, 1997 (e-mail: giering@dkrz.de).
22. M. L. J. Rightley, R. J. Henninger, and K. M. Hanson, "Adjoint differentiation of hydrodynamic codes," in CNLS Research Highlights, Center for Nonlinear Studies, Los Alamos National Laboratory, April, 1998 (WWW: http://culs.lanl.gov/Publications/highlights.html).
23. G. H. Golub and C. F. Van Loan, Matrix Computations - Third Edition, Johns Hopkins University, Baltimore, 1996.
24. R. M. Neal, Bayesian Learning for Neural Networks, Springer, New York, 1996.