Bayesian Analysis of the Loai Distribution with Conjugate Priors: Methodology and Applications

This research explores the application of Bayesian predictive modeling in the context of the Loai distribution, utilizing a conjugate prior approach. The Loai distribution, a versatile probability distribution, finds applications in various fields such as finance, biology, and engineering. Bayesian methods, with their ability to incorporate prior knowledge, offer a powerful framework for predictive modelling. The study focuses on employing a conjugate prior, which simplifies the computational aspects of Bayesian inference. This approach facilitates efficient updating of beliefs, making it particularly suitable for real-time predictions and decision-making. This research applies Bayesian predictive modeling to the Loai distribution using a conjugate prior, which simplifies computations and enables efficient belief updates. Applications in fields like finance, biology, and engineering demonstrate the model's realworld utility.