Bayesian Measures of Model Complexity and Fit

  title={Bayesian Measures of Model Complexity and Fit},
  author={David J. Spiegelhalter and Nicola G. Best and Bradley P. Carlin and Angelika van der Linde},
We consider the problem of comparing complex hierarchical models in which the number of parameters is not clearly defined. Using an information theoretic argument we derive a measure pD for the effective number of parameters in a model as the difference between the posterior mean of the deviance and the deviance at the posterior means of the parameters of interest. In general pD approximately corresponds to the trace of the product of Fisher’s information and the posterior covariance, which in… CONTINUE READING
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