In Bayesian inference, the acquisition of prior distributions plays a fundamental role. While authorized priors need not conform to traditional probability densities and may be improper priors, obtaining proper prior densities remains a challenge in the Bayesian literature. This article explores a set of conditions that enable the establishment of specific assumptions, ensuring that maximum entropy priors and restricted reference priors become proper and transform into probability density priors. By examining these conditions, this study contributes to the advancement of proper prior acquisition in Bayesian analysis.
Ghatari, A., & Tabrizi, E. (2022). Conditions for interior based constrained prior distributions to ensure probability density. Journal of Statistical Modelling: Theory and Applications, 3(2), 145-155. doi: 10.22034/jsmta.2023.20440.1110
MLA
Amirhossein Ghatari; Elham Tabrizi. "Conditions for interior based constrained prior distributions to ensure probability density", Journal of Statistical Modelling: Theory and Applications, 3, 2, 2022, 145-155. doi: 10.22034/jsmta.2023.20440.1110
HARVARD
Ghatari, A., Tabrizi, E. (2022). 'Conditions for interior based constrained prior distributions to ensure probability density', Journal of Statistical Modelling: Theory and Applications, 3(2), pp. 145-155. doi: 10.22034/jsmta.2023.20440.1110
VANCOUVER
Ghatari, A., Tabrizi, E. Conditions for interior based constrained prior distributions to ensure probability density. Journal of Statistical Modelling: Theory and Applications, 2022; 3(2): 145-155. doi: 10.22034/jsmta.2023.20440.1110
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