Bayesian Approach of Modified Half - Cauchy Chen Distribution
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250-261
Digital Object Identifier:
10.58578/mikailalsys.v3i1.5124
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Authors:
Lal Babu Sah Telee1*, Arun Kumar Chaudhary2, Murari Karki3 
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Abstract
In this article, we explore a Bayesian framework for parameter estimation and model evaluation using a specific probability model, referred to as the MHCC Distribution. The model defines the likelihood of observed data through parameters α, β, and θ. We employ both Gibbs sampling and Stan, a state-of-the-art platform for Bayesian statistical modeling, to estimate the parameters of the model. A key focus is on validating the model through posterior predictive checks. Our analysis also includes a detailed evaluation of model diagnostics, including trace plots, autocorrelation plots, and Gelman-Rubin convergence diagnostics. The goal of this work is to provide a comprehensive approach to model fitting, diagnostics, and validation in Bayesian inference.
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Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
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