An introduction to infinite HMMs for single molecule data analysis

TL;DR

This paper introduces the infinite hidden Markov model (iHMM), a powerful extension of traditional HMMs that automatically determines the number of states, with a focus on applications in single molecule data analysis and implementation details.

Contribution

It provides a conceptual and practical introduction to iHMMs, emphasizing their applicability in biophysics and offering accessible implementation resources.

Findings

iHMM can analyze data without predefining the number of states

The paper offers a detailed explanation of iHMM implementation

A code for iHMM analysis is made freely available

Abstract

The hidden Markov model (HMM) has been a workhorse of single molecule data analysis and is now commonly used as a standalone tool in time series analysis or in conjunction with other analyses methods such as tracking. Here we provide a conceptual introduction to an important generalization of the HMM which is poised to have a deep impact across Biophysics: the infinite hidden Markov model (iHMM). As a modeling tool, iHMMs can analyze sequential data without a priori setting a specific number of states as required for the traditional (finite) HMM. While the current literature on the iHMM is primarily intended for audiences in Statistics, the idea is powerful and the iHMM's breadth in applicability outside Machine Learning and Data Science warrants a careful exposition. Here we explain the key ideas underlying the iHMM with a special emphasis on implementation and provide a description of a code we are making freely available. In a companion article, we provide an important extension of the iHMM to accommodate complications such as drift.