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9 - Model Selection

Published online by Cambridge University Press:  11 May 2017

Joseph M. Hilbe
Affiliation:
Jet Propulsion Laboratory, California Institute of Technology
Rafael S. de Souza
Affiliation:
Eötvös Loránd University, Budapest
Emille E. O. Ishida
Affiliation:
Université Clermont-Auvergne (Université Blaise Pascal), France
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Bayesian Models for Astrophysical Data
Using R, JAGS, Python, and Stan
, pp. 262 - 275
Publisher: Cambridge University Press
Print publication year: 2017

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References

Gelman, A., J., Carlin, H., Stern, D., Dunson, A., Vehtari, and D., Rubin (2013). Bayesian Data Analysis, Third Edition. Chapman & Hall/CRC Texts in Statistical Science. Taylor & Francis.
Gelman, A., J., Hwang, and A., Vehtari (2014). “Understanding predictive information criteria for Bayesian models.” Statist. Comput. 24(6), 997–1016. DOI:10.1007/s11222-013-9416-2.
O'Hara, R. B. and M. J, Sillanpää (2009). “A review of Bayesian variable selection methods: what, how and which.” Bayesian Anal. 4(1), 85–117. DOI: 10.1214/09-ba403.
Spiegelhalter, D. J., N. G., Best, B. P., Carlin, and A., Van der Linde (2002). “Bayesian measures of model complexity and fit.” J. Royal Statist. Soc.:Series B (Stastistical Methodology) 64(4), 583–639. DOI: 10.1111/1467-9868.00353.
Vehtari, A. and J., Ojanen (2012). “A survey of Bayesian predictive methods for model assessment, selection and comparison.” Statist. Surv. 6, 142–228. DOI: 10.1214/12-SS102.

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