# Infinite Mixture Model of Markov Chains

## TL;DR

This paper introduces a Bayesian nonparametric mixture model for categorical time series that captures multiple underlying patterns, improving segmentation and prediction with interpretable results.

## Contribution

It extends hierarchical hidden Markov models by incorporating structural information and offers an efficient inference scheme for better pattern detection.

## Key findings

- Model effectively identifies underlying patterns in data.
- Achieves superior segmentation and prediction performance.
- Results are interpretable and applicable to real-world data.

## Abstract

We propose a Bayesian nonparametric mixture model for prediction- and information extraction tasks with an efficient inference scheme. It models categorical-valued time series that exhibit dynamics from multiple underlying patterns (e.g. user behavior traces). We simplify the idea of capturing these patterns by hierarchical hidden Markov models (HHMMs) - and extend the existing approaches by the additional representation of structural information. Our empirical results are based on both synthetic- and real world data. They indicate that the results are easily interpretable, and that the model excels at segmentation and prediction performance: it successfully identifies the generating patterns and can be used for effective prediction of future observations.