Construction of prior probability distribution using a mollified functional

Autori

  • S.J. Yaga*† * Department of Mathematical Sciences, University of Maiduguri, Maiduguri, Nigeria
  • N.P. Dibal* Department of Statistics, University of Agriculture, Makurdi, Nigeria
  • I.E. Gongsin* College of Medical Sciences, Department of Community Medicine, University of Maiduguri, Maiduguri, Nigeria
  • H.R. Bakari*
  • S.C. Nwaosu‡
  • Zara Wudiri§

##semicolon##

mollifier##common.commaListSeparator## mfdip##common.commaListSeparator## invariance property##common.commaListSeparator## prior probability distribution##common.commaListSeparator## parametric functions

Abstrakt

In this paper, we proposed a new class of prior distribution, the Mollified Functional Data Informative Prior using the convolution of standard scaled mollified functional and sampling distribution of the data. We also show that the MFDIP possesses the invariance property to reparameterization and apply the MFDIP to derive the prior for exponential probability distribution.

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Publikované

2023-09-30

##submission.howToCite##

Yaga*†, S. ., Dibal*, N. ., Gongsin*, I. ., Bakari*, H. ., Nwaosu‡, S. ., & Wudiri§, Z. . (2023). Construction of prior probability distribution using a mollified functional . International Journal of Mathematical Analysis and Modelling, 5(4). Cit z https://tnsmb.org/journal/index.php/ijmam/article/view/70

Číslo

Diel 5 Číslo 4 (2022): Volume 5, Issue 4 (Nov.) 2022

Sekcia

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