NoMoPy: Noise Modeling in Python
Computation
2023-11-02 v1 Computational Engineering, Finance, and Science
Mathematical Software
Machine Learning
Abstract
NoMoPy is a code for fitting, analyzing, and generating noise modeled as a hidden Markov model (HMM) or, more generally, factorial hidden Markov model (FHMM). This code, written in Python, implements approximate and exact expectation maximization (EM) algorithms for performing the parameter estimation process, model selection procedures via cross-validation, and parameter confidence region estimation. Here, we describe in detail the functionality implemented in NoMoPy and provide examples of its use and performance on example problems.
Cite
@article{arxiv.2311.00084,
title = {NoMoPy: Noise Modeling in Python},
author = {Dylan Albrecht and N. Tobias Jacobson},
journal= {arXiv preprint arXiv:2311.00084},
year = {2023}
}
Comments
55 pages, 68 figures, citation paper