Abstract
Since the seminal paper by Kirkpatrick, Gelatt and Vechhi (1983), a number of papers in the scientific literature refer to simulated annealing as a powerful random optimization method which promises to deliver, within reasonable computing times, optimal or nearly optimal solutions to complex decision problems hitherto forbidding. The algorithm, which uses the physical process of annealing as a metaphor, is special in that, at each iteration, one may move with positive probability to solutions with higher values of the function to minimize, rather than directly jumping to the point with the smallest value within the neighborhood, thus drastically reducing the chances of getting trapped in local minima. This paper describes a variation of this algorithm within the Bayesian context of minimizing the expected loss in complex decision problems. The procedure is then applied to the identification of good designs for political sample surveys, aimed at predicting the behaviour of the electorate in future elections.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bernardo, J. M. (1979). Expected information as expected utility. Ann. Statist. 7. 636–690.
Bernardo, J. M., Ferrándiz, J. R. and Smith, A. F. M. (1985). The foundations of decision theory: An intuitive, operational approach with mathematical extensions. Theory and Decision 19, 127–150.
Bernardo, J. M. and Girón, F. J. (1992). Robust sequential prediction form non-random samples: the election night forecasting case. Bayesian Statistics 4 (J. M. Bernardo, J. O. Berger, A. P. Dawid and A. F. M. Smith, eds.), Oxford: University Press, 61–77, (with discussion).
Fishburn, P. C. (1981). Subjective expected utility: a review of normative theories. Theory and Decision 13, 139–199.
Good, I. J. (1952). Rational decisions. J. Roy. Statist. Soc. B 14, 107–114.
Haines, L. M. (1987). The application of the annealing algorithm to the construction of exact optimal designs for linear regression models. Technometrics 29, 439–447.
Kirkpatrick, S., Gelati, C. D. and Vecchi, M. P. (1983). Optimization by simulated annealing. Science 220, 671–680.
Lundy, M. (1985). Applications of the annealing algorithm to combinatorial problems in statistics. Biometrika 72, 191–198.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1992 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bernardo, J.M. (1992). Simulated Annealing in Bayesian Decision Theory. In: Dodge, Y., Whittaker, J. (eds) Computational Statistics. Physica, Heidelberg. https://doi.org/10.1007/978-3-662-26811-7_75
Download citation
DOI: https://doi.org/10.1007/978-3-662-26811-7_75
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-662-26813-1
Online ISBN: 978-3-662-26811-7
eBook Packages: Springer Book Archive