ICON: An Adaptation of Infinite HMMs for Time Traces with Drift.

ABSTRACT: Bayesian nonparametric methods have recently transformed emerging areas within data science. One such promising method, the infinite hidden Markov model (iHMM), generalizes the HMM that itself has become a workhorse in single molecule data analysis. The iHMM goes beyond the HMM by self-consistently learning all parameters learned by the HMM in addition to learning the number of states without recourse to any model selection steps. Despite its generality, simple features (such as drift), common to single molecule time traces, result in an overinterpretation of drift and the introduction of artifact states. Here we present an adaptation of the iHMM that can treat data with drift originating from one or many traces (e.g., Förster resonance energy transfer). Our fully Bayesian method couples t

SUBMITTER: Sgouralis I 

PROVIDER: S-EPMC5443975 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature