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Bayesian predictive analysis for Lindley distribution with various priors
Abstract
Bayesian analysis provides a distinctive approach to forecasting by allowing the integration of current data with existing predictions. This method improves the reliability of predictions by effectively addressing uncertainties, offering an edge over traditional forecasting techniques. In this study, we derive the predictive density using the Lindley distribution under various prior assumptions, including conjugate, quasi, uniform, and non-informative priors. The results underscore the effectiveness of Bayesian predictive methods in generating more accurate and credible predictions.
Keywords
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