Generalizing HMMs to Continuous Time for Fast Kinetics: Hidden Markov Jump Processes.

ABSTRACT: The hidden Markov model (HMM) is a framework for time series analysis widely applied to single-molecule experiments. Although initially developed for applications outside the natural sciences, the HMM has traditionally been used to interpret signals generated by physical systems, such as single molecules, evolving in a discrete state space observed at discrete time levels dictated by the data acquisition rate. Within the HMM framework, transitions between states are modeled as occurring at the end of each data acquisition period and are described using transition probabilities. Yet, whereas measurements are often performed at discrete time levels in the natural sciences, physical systems evolve in continuous time according to transition rates. It then follows that the modeling assumptions

SUBMITTER: Kilic Z 

PROVIDER: S-EPMC7896036 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature