Bayesian Inference under Small Sample Sizes Using General Noninformative Priors

Bayesian Inference under Small Sample Sizes Using General Noninformative Priors

This paper proposes a Bayesian inference method for problems with small sample sizes. A general type of noninformative prior is proposed to formulate the Bayesian posterior. It is shown that this type of prior can represent a broad range of priors such as classical noninformative priors and asymptot...

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Bibliographic Details
Main Authors: Jingjing He, Wei Wang, Min Huang, Shaohua Wang, Xuefei Guan
Format: Article
Language:English
Published: MDPI AG 2021-11-01
Series:Mathematics
Subjects:
Bayesian inference
noninformative prior
Jeffreys’ prior
invariant
Online Access:https://www.mdpi.com/2227-7390/9/21/2810

Internet

https://www.mdpi.com/2227-7390/9/21/2810

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