Contributions to Bayesian Nonparametric methods with applications - Ayed, MF (2020)
The Bayesian methodology provides a compelling framework that offers an intuitive yet practical approach to model the world, allowing refinement of the statistician’s prior knowledge in light of incoming empirical data. Any unknown quantity is regarded as random, and its updated knowledge, called the posterior distribution, quantifies the remaining uncertainty therein. The choice of the prior distribution is at the core of Bayesian modeling and plays a primary role in the model’s performance. Bayesian Nonparametric (BNP) modeling utilizes infinitely many parameters to capture intricate patterns of real-world phenomena and adapts to the complexity of the data.