If you need an accessible version of this item please contact JSTOR User Support

Bayesian Model Choice: Asymptotics and Exact Calculations

A. E. Gelfand and D. K. Dey
Journal of the Royal Statistical Society. Series B (Methodological)
Vol. 56, No. 3 (1994), pp. 501-514
Published by: Wiley for the Royal Statistical Society
Stable URL: http://www.jstor.org/stable/2346123
Page Count: 14
  • Download PDF
  • Cite this Item

You are not currently logged in.

Access your personal account or get JSTOR access through your library or other institution:

login

Log in to your personal account or through your institution.

If you need an accessible version of this item please contact JSTOR User Support
Bayesian Model Choice: Asymptotics and Exact Calculations
Preview not available

Abstract

Model determination is a fundamental data analytic task. Here we consider the problem of choosing among a finite (without loss of generality we assume two) set of models. After briefly reviewing classical and Bayesian model choice strategies we present a general predictive density which includes all proposed Bayesian approaches that we are aware of. Using Laplace approximations we can conveniently assess and compare the asymptotic behaviour of these approaches. Concern regarding the accuracy of these approximations for small to moderate sample sizes encourages the use of Monte Carlo techniques to carry out exact calculations. A data set fitted with nested non-linear models enables comparisons between proposals and between exact and asymptotic values.

Page Thumbnails

  • Thumbnail: Page 
[501]
    [501]
  • Thumbnail: Page 
502
    502
  • Thumbnail: Page 
503
    503
  • Thumbnail: Page 
504
    504
  • Thumbnail: Page 
505
    505
  • Thumbnail: Page 
506
    506
  • Thumbnail: Page 
507
    507
  • Thumbnail: Page 
508
    508
  • Thumbnail: Page 
509
    509
  • Thumbnail: Page 
510
    510
  • Thumbnail: Page 
511
    511
  • Thumbnail: Page 
512
    512
  • Thumbnail: Page 
513
    513
  • Thumbnail: Page 
514
    514