Understanding predictive information criteria for Bayesian models

Author:

Gelman Andrew,Hwang Jessica,Vehtari Aki

Publisher

Springer Science and Business Media LLC

Subject

Computational Theory and Mathematics,Statistics, Probability and Uncertainty,Statistics and Probability,Theoretical Computer Science

Reference40 articles.

1. Aitkin, M.: Statistical Inference: an Integrated Bayesian/Likelihood Approach. Chapman & Hall, London (2010)

2. Akaike, H.: Information theory and an extension of the maximum likelihood principle. In: Petrov, B.N., Csaki, F. (eds.) Proceedings of the Second International Symposium on Information Theory, pp. 267–281. Akademiai Kiado, Budapest (1973). Reprinted in: Kotz, S. (ed.) Breakthroughs in Statistics, pp. 610–624. Springer, New York (1992)

3. Ando, T., Tsay, R.: Predictive likelihood for Bayesian model selection and averaging. Int. J. Forecast. 26, 744–763 (2010)

4. Bernardo, J.M.: Expected information as expected utility. Ann. Stat. 7, 686–690 (1979)

5. Bernardo, J.M., Smith, A.F.M.: Bayesian Theory. Wiley, New York (1994)

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