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Fundamentals of Nonparametric Bayesian Inference

"Nonparametric Bayesian inference is a statistical framework where models can grow in complexity as data increases, bypassing the need to fix the number of parameters beforehand. By employing infinite-dimensional prior distributions over functions or probability measures, practitioners can let data dictate model structure, achieving flexibility in density estimation, clustering, and regression. Introduction to Nonparametric Bayesian Inference Traditional parametric statistics requires the modeler to specify a fixed number of parameters in advance—such as the exact number of components in a Gaussian mixture model or the order of a polynomial re..."

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