The calibration of P-values, posterior Bayes factors and the AIC from the posterior distribution of the likelihood
- Cite this article as:
- Aitkin, M. Statistics and Computing (1997) 7: 253. doi:10.1023/A:1018550505678
- 340 Downloads
The posterior distribution of the likelihood is used to interpret the evidential meaning of P-values, posterior Bayes factors and Akaike's information criterion when comparing point null hypotheses with composite alternatives. Asymptotic arguments lead to simple re-calibrations of these criteria in terms of posterior tail probabilities of the likelihood ratio. (‘Prior’) Bayes factors cannot be calibrated in this way as they are model-specific.