A Bayesian 2 test for goodness-of-fit Academic Article uri icon


  • This article describes an extension of classical 2 goodness-of-fit tests to Bayesian model assessment. The extension, which essentially involves evaluating Pearson's goodness-of-fit statistic at a parameter value drawn from its posterior distribution, has the important property that it is asymptotically distributed as a 2 random variable on K - 1 degrees of freedom, independently of the dimension of the underlying parameter vector. By examining the posterior distribution of this statistic, global goodness-of-fit diagnostics are obtained. Advantages of these diagnostics include ease of interpretation, computational convenience and favorable power properties. The proposed diagnostics can be used to assess the adequacy of a broad class of Bayesian models, essentially requiring only a finite-dimensional parameter vector and conditionally independent observations. Institute of Mathematical Statistics, 2004.

published proceedings

  • The Annals of Statistics

author list (cited authors)

  • Johnson, V. E.

citation count

  • 97

complete list of authors

  • Johnson, Valen E

publication date

  • January 2004