A non-parametric bayesian model is a bayesian model with infinite number of parameters.

These parameters will be learnt conditionally on the dataset availability. Meaning, as more data points are added to the set, we can potentially use more parameters to describe the dataset.

Examples of models/applications:

  1. Dirichlet Process K-means
  2. Gaussian processes for non-linear regression