________________________________________ *Corresponding author: Email: pamdouglasjah@gmail.com; Asian J. Prob. Stat., vol. 20, no. 4, pp. 68-81, 2022 Asian Journal of Probability and Statistics Volume 20, Issue 4, Page 68-81, 2022; Article no.AJPAS.83451 ISSN: 2582-0230 _______________________________________________________________________________________________________________________________________ Bayesian Estimation of a Scale Parameter of the Gumbel-Lomax Distribution Using Informative and Non Informative Priors Douglas Jah Pam a* , Kazeem Eyitayo Lasisi a , Umar Farouk Abbas a , Mustapha Tijani b , Sheyi Mafolasire c and Blessing Uke Agbor a a Department Mathematical Sciences, Abubakar Tafawa Balewa University, P.M.B. 0248, Bauchi, Nigeria. b Department of Statistics, University of Ilorin, Nigeria. c Department of Statistics, Federal College of Forestry, Jos, Nigeria. Authors’ contributions This work was carried out in collaboration among all authors. All authors read and approved the final manuscript. Article Information DOI: 10.9734/AJPAS/2022/v20i4440 Open Peer Review History: This journal follows the Advanced Open Peer Review policy. Identity of the Reviewers, Editor(s) and additional Reviewers, peer review comments, different versions of the manuscript, comments of the editors, etc are available here: https://www.sdiarticle5.com/review-history/83451 Received: 26/02/2022 Accepted: 19/04/2022 Published: 03/12/2022 __________________________________________________________________________________ Abstract Estimating the scale parameter of the Gumbel-Lomax Distribution using the Bayesian method of estimation and evaluating the estimators by assuming two non-informative prior distributions and one informative prior distribution is very important for the general application of the Gumbel-Lomax distribution. These estimators are obtained using the squared error loss function ( SELF), Quadratic loss function (QLF) and precautionary loss function (PLF). The posterior distributions of the scale parameter of the Gumbel-Lomax distribution are derived and the Estimators are also obtained using the above mentioned priors and loss functions. Furthermore, a simulation using a package in R software is carried out to assess the performance of the estimators by making use of the Mean Squared Errors of the Estimators under the Bayesian approach and Original Research Article