Bayesian nonparametrics

Bayesian nonparametrics is a flexible statistical approach that allows models to adapt and grow based on the data available, without assuming a fixed number of parameters. Unlike traditional models, which define a specific structure in advance, Bayesian nonparametrics uses a probabilistic framework that can accommodate an unknown number of groups or features. This is particularly useful in complex data scenarios where patterns are not well understood. Essentially, it allows for more nuanced insights by building models that can dynamically capture the underlying structure of the data as more information becomes available.