A Tutorial on Bayesian Nonparametric Models

TL;DR

This tutorial introduces Bayesian nonparametric methods that automatically determine model complexity, addressing the challenge of model selection in clustering and factor analysis.

Contribution

It provides a high-level overview of Bayesian nonparametric techniques with practical examples, highlighting their ability to adapt model complexity to data.

Findings

Bayesian nonparametric methods effectively determine the number of clusters.

These methods adapt model complexity based on data without pre-specification.

Applications include mixture models and factor analysis.

Abstract

A key problem in statistical modeling is model selection, how to choose a model at an appropriate level of complexity. This problem appears in many settings, most prominently in choosing the number ofclusters in mixture models or the number of factors in factor analysis. In this tutorial we describe Bayesian nonparametric methods, a class of methods that side-steps this issue by allowing the data to determine the complexity of the model. This tutorial is a high-level introduction to Bayesian nonparametric methods and contains several examples of their application.